The Logic of Science

Evolutionary mechanisms part 3: the benefits of mutations

Posted on November 9, 2015 by Fallacy Man

Mutations have an almost universally negative connotation (except in the context of superheros). When people hear the word, they instantly think of disabilities, bizarre disfigurements, and grotesque scenes from science fictions. The reality is, however, quite a bit different. Although there are extremely harmful mutations, they are actually in the minority, and mutations can be a wonderful thing. You see, mutations are the one and only way of generating truly new genetic information. In contrast, selection and genetic drift (two of the dominant evolutionary mechanisms) actually remove variation, and gene flow (the final mechanism) can only shuffle existing alleles among populations. So, without mutations there would be no variation, which means that there would be nothing for selection to act on, which means that populations would be unable to adapt to changes in the environment and would ultimately go extinct. To put it simply, for most species, sustained life on planet earth would not be possible without mutations.

Given how vital mutations are, it is important to have at least a basic understanding of them. Therefore, in this post, I am briefly going to explain why most mutations aren’t harmful and go over some of the different ways that they can create new genetic information.

What is a mutation?
First, I need to specify what I mean by “mutation.” Mutations are simply any changes in an organism’s DNA. They generally occur when a cell is replicating, and they can involve deleting bases, adding bases, or rearranging bases (remember, all DNA is made from combinations of four bases: adenine (A), guanine (G), cytosine (C), and thymine (T)).

We can group mutations into two broad categories (somatic and germline), but only germline mutations act as an evolution mechanism. Somatic mutations occur in body cells and do not get passed onto offspring. For example, if you frequently use a cancer coffin (aka tanning bed) you will likely mutate the DNA in your skin cells, ultimately resulting in skin cancer. That type of mutation is not, however, an evolutionary mechanism because it doesn’t change the allele frequencies of the population.

In order for a mutation to act as an evolutionary mechanism, it has to involve germ cells (eggs or sperm). Mutations in those cells will get passed onto the offspring, thus altering the gene frequencies of the population. So, when we talk about mutations as an evolutionary mechanism, we are only talking about germline mutations, not somatic mutations.

Note: you could argue that somatic mutations still alter gene frequencies because they may kill an individual, thus removing the individual’s alleles and altering the allele frequencies (cancer is a good example of this), but in that case, the mutation itself isn’t the mechanism, rather natural selection is the mechanism. In other words, it’s selection that actually removes the individual and modifies the allele frequencies, not the mutation.

Neutral and harmful mutations still cause evolution
It’s important to note that evolution is not inherently beneficial. Selection is always beneficial (for the immediate generation), but evolution itself is simply a change in allele frequencies, and there is no reason why that change has to be a beneficial one (indeed, genetic drift is generally bad). Therefore, all germline mutations that make it into the population represent evolutionary events, regardless of whether they are harmful, neutral, or beneficial.

Many mutations are neutral
It is an extremely common misconception that most mutations are harmful. In reality, for many species, most of them are essentially neutral (i.e., they do not benefit or harm the organism, and, therefore, selection does not act on them). For example, Nachman and Crowell (2000) estimated that for humans, only 1.7% of the mutations that occurred each generation were harmful; however, the number and nature of neutral vs. beneficial. vs. harmful mutations varies greatly among species (see Eyre-Walker et al. 2007 for a review).

There are several important reasons that many mutations are neutral. First, it is important to remember that mutations are completely random. There is no force controlling what mutations occur, and what an organism actually needs has no effect on what mutations will arise.

Second, the majority of organisms have large non-functional sections of DNA. In other words, there are big chunks of DNA that do not actually do anything (or at least do very little). The amount of DNA that is nonfunctional varies among species and is often debated. For example, there is significant controversy about how much of the human genome is actually functional, with estimates ranging from 8.2% (Rands et al. 2014) all the way to 80% (ENCODE Project Consortium) depending largely on how “function” is defined (you can find a brief discussion of the controversy here); however, regardless of the exact amount, everyone agrees that some portions of the genetic code don’t seem to do anything, which also means that mutations in those regions tend not to do anything.

The third reason has to do with the nature of proteins. DNA codes for amino acids, and amino acids string together to form proteins. Both the amino acids and the proteins are, however, redundant. Amino acids are formed by three bases, but the third base is usually irrelevant. For example, GAA, GAG, GAT, and GAC all code for the amino acid leucine. So a mutation that changes the third base will have no effect on the final protein. Further, proteins themselves are generally redundant, and there are multiple combinations of amino acids that will make the same protein.

Fourth, even if the protein itself is modified, that may not actually affect the organism. Indeed, all of the variation that you see in organisms is caused by mutations, and most of them are neutral. Why, for example, do only some people have attached earlobes, cleft chins, dimples, widow’s peaks, blue eyes, etc.? Quite simply, because at some point in the history of human evolution, mutations arose and spread through a population via genetic drift, ultimately resulting in variation for those traits; however, none of those traits affect an individuals ability to survive or reproduce. Things like the ability to curl your tongue like a taco don’t affect your evolutionary fitness, and are, therefore, neutral mutations. In reality, all of us are a massive collection of mutations.

Finally, remember that natural selection simply adapts populations for their current environment, so whether or not a mutation is beneficial will often depend on the environment and conditions that the organism is experiencing. For example, a mutation for bright red color may be very useful for a population in which females are selecting mates based on color, but that same mutation may be very harmful in a population in which individuals need to be camouflaged to avoid being eaten by predators.

Some mutations are beneficial
Some mutations are admittedly harmful, but selection eliminates or at least reduces them. Further, many mutations are beneficial, and selection can and does act on those, resulting in them increasing in frequency within the population.

Mutation accumulation experiments
There have been several excellent laboratory studies which have measured the formation and accumulation of beneficial mutations, and in many cases, the beneficial mutations arose more quickly than expected (Shaw et al. 2002, 2003; Joseph and Hall 2004; Perfeito et al. 2007; you can find a review and more detailed explication of these experiments in Halligan and Keightley 2009). In short, they put the study population under some experimental condition, then let the colonies do their thing for several generations. After the allotted number of generations, the researchers analyzed the colonies by comparing them to a control colony which was maintained in the ancestral condition. Thus, they could see the formation of new genetic information (i.e., mutations), and they could test whether or not the were beneficial by seeing if the mutated colonies grew and survived better than the originals. These studies very clearly demonstrate that beneficial mutations not only occur, but occur frequently enough to have adaptive significance. Therefore, if you honestly think that beneficial mutations don’t occur/are too rare for evolution, you are willfully ignorant of the facts.

Some creationists object to these studies by arguing that they were done in the lab, so we don’t actually know that beneficial mutations occur in nature, but this objection is completely invalid as it totally ignores the nature of mutations. The researchers generally don’t do anything to induce mutations. Rather, they simply put the organisms into a novel environment and let nature take it’s course. In other words, they aren’t constantly manipulation each generation. So, these studies are an excellent analog of nature, and there is absolutely no reason to think that they same processes don’t occur in nature. Remember, mutations are random. There is no mechanism that would cause beneficial mutations to spontaneously arise in a lab, but not in nature.

A mutation for HIV resistance in humans
In addition to the experimental studies, we also have evidence of the existence of beneficial mutations in humans. Perhaps most prominently, a deletion in the CKR5 gene results in resistance to HIV infections (Dean et al. 1996; Sullivan et al. 2001). This is very clearly a mutation (it is a deletion of several base pairs), yet it is also very clearly beneficial.

Bacteria evolve the ability to process citrate
There are many other examples of beneficial mutations that I could give (for example this really neat study describing a mutation that allowed blow flies to evolve pesticide resistance [Newcomb et al. 1997]), but I want to focus on just one final example. For all of the examples that I have given thus far, creationists typically respond with nonsense like, “those aren’t actually mutations, they are just part of the variation that God created when he made the earth.” This response is an ad hoc fallacy, it is logically inconsistent with the fact that creationists accept the results when identical methods show that some diseases are caused by mutations, and it doesn’t make any sense at all given that creationists believe that all modern animals evolved from the limited survivors of Noah’s flood (which would have had essentially no genetic variation). Nevertheless, let’s just say for sake of argument that creationists’ response was valid. This final example completely defeats that argument, because it is clearly and undeniably a beneficial mutation.

I am of course referring to the long term study of E. coli by Richard Lenski. He and his students did something amazingly clever. They started 12 bacterial colonies from an original clone, then watched them develop over thousands of generations. They didn’t interfere, they just let them do their thing, and eventually, something remarkable happened in one of the colonies. The bacteria were being grown on medium that included citrate, but E. coli is incapable of metabolizing (eating) citrate in the aerobic conditions under which they were being grown. Several thousand generations in, however, one colony suddenly became larger and began growing rapidly, and when the colony was examined, it was discovered that they had mutated the ability to consume citrate! Several lines of evidence demonstrate beyond the slightest shadow of a doubt that this was a mutation, not pre-existing variation. First, all 12 colonies were started from a single bacteria, so there was no variation. All of the bacteria were genetically identical at the start. In other words, if this trait was already present at the start of the experiment, it would have been in every bacteria in every colony from day 1, yet it only appeared in one colony, and it did not appear for thousands of generations. Further, the researchers saved and froze samples from each generation, so they were able to go back through them and pinpoint exactly when this mutation first arose (Blount et al. 2008).

You could not ask for a more clear or undeniable example of a beneficial mutation, but, unsurprisingly, creationists were not thrilled by this result. You can read the most famous exchange on this issue here. There is also a popular article on creation.com which takes issue with this result. I eventually plan on spending an entire post debunking their nonsense, but in short, they argue that this mutation still doesn’t explain the origins of new genetic material. However, as I will explain below, that response completely misses the point, and misrepresents how mutations actually work.

All mutations create new genetic information
Another very common misconception is that we don’t know of any mechanism for creating new genetic information. That claim is blatantly false, because mutations are, by definition, new genetic information. Some of them even work by very directly adding information. For example, some mutations are called “additions” and they are exactly what they sound like: they add extra bases to the DNA.

Other mutations don’t directly increase the amount of DNA, but they still add information. I think that this is where some of the confusion comes from: adding information does not necessarily mean making more DNA. Consider, for example, a mutation known as a substitution. This is where the wrong base gets used. So, for example, one section of DNA may have been supposed to be AGT, but instead a mutation happened and it ended up being CGT. Thus, the C was substituted for the A. In this case, we have not actually “added” genetic material, but we have still created new genetic material, because AGT and CGT will not produce the same amino acid.

Think of it this way. The DNA bases are like letters of an alphabet, and we string those letters together to form words (amino acids) and we combine words to form sentences (proteins). Now, consider the following sentence: “the dog ate the cats.” Imagine that a mistake (mutation) happened while copying that sentence so that the copy read, “the dog ate the bats.” All that happened was that one letter got substituted, but this sentence now tells us something totally different. It is new information, even though the number of letters hasn’t changed.

Further, some mutations (called deletions) actually remove DNA, but they still create new information. Let’s use the cat sentence again, but this time, suppose the that “s” got deleted, so the sentence became, “the dog at the cat.” This sentence is still different. Now we have one cat being eaten instead of several. It has a new meaning, even though it lost a letter. Even so, a mutation that removes a base will often result in an entirely new protein. Thus, new information is formed even though DNA is lost.

The mutation on the CKR5 gene that I mentioned earlier is a great example of this. The mutation actually deletes several bases, but that deletion results in a new code which ultimately results in resistance to HIV. So the loss of DNA actually creates new genetic information which results in a new and important function.

Conclusion
Despite the many myths about mutations (mostly perpetuated by creationists) mutations aren’t always harmful. Most of them are actually neutral, and beneficial ones do occur. Further, mutations are extremely important because they create new genetic information (even when they delete bases), and without mutations, there would be no variation, and evolution would grind to a halt. Ultimately, mutations are responsible for all of the variation that we see, and all of us are mutant freaks.

Other posts on evolutionary mechanisms:

Literature cited

Blount et al. 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences 105:7899–7906.

Dean et al. 1996. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Science 273:1856–1862.

ENCODE Project Consortium. 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 48957–74.

Eyre-Walker et al. 2007. The distribution of fitness effects of new mutations. Nature Reviews Genetics 8:610–618.

Halligan and Keightley. 2009. Spontaneous mutation accumulation studies in evolutionary genetics. Annual Review of Ecology, Evolution, and Systematics 40:151–172.

Joseph and Hall. 2004. Spontaneous mutations in diploid Saccharomyces cerevisiae more beneficial than expected. Genetics 168:1817–1825.

Nachman and Crowell. 2000. Estimate of the mutation rate per nucleotide in humans. Genetics 156:297–304.

Newcomb et al. 1997. A single amino acid substitution converts a carboxylesterase to an organophosphorus hydrolase and confers insecticide resistance on a blowfly. Proceedings of the National Academy of Sciences 94:7464–7468.

Perfeito et al. 2007. Adaptive mutations in bacteria: high rate and small effects. Science 317:813–815.

Rands et al. 2014. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genetics 10:e1004525.

Shaw et al. 2002. A comprehensive model of mutations affecting fitness and inferences for Arabidopsis thaliana. Evolution 56:453–463.

Shaw et al. 2003. What fraction of mutations reduces fitness? A reply to Keightley and Lynch. Evolution 57:686–689.

Sullivan et al. 2001. The coreceptor mutation CCR5Δ32 influences the dynamics of HIV epidemics and is selected for by HIV. Proceedings of the National Academy of Sciences 98:10214–10219.

Posted in Science of Evolution, Uncategorized | Tagged Bad arguments, creationism, evolution, mutations | Comments Off

The logical paradox of ghost hunting

Posted on November 2, 2015 by Fallacy Man

Many people believe in the paranormal, and a great deal of time and effort is spent searching for evidence of it. Indeed, shows like “Ghost Hunters” are extremely popular, and the notion of using scientific equipment to detect the supernatural is well ingrained into our literature, movies, and culture more generally. The reality is, however, the ghost hunting is a perfect case study in pseudoscience, and it is based on a series of logical fallacies and amusing paradoxes.

Most obviously, ghost hunting (along with related pseudoscientific ventures such as UFO spotting, searches for Big Foot and Nessy, Creation Research, etc.) suffers a serious flaw which automatically removes it from the realm of science. Namely, it starts with a conclusion (i.e., ghosts exist), then tries to prove that conclusion. In contrast, real science always starts with the evidence, then forms a conclusion based on that evidence. This distinction is extremely important, because if you start with a conclusion, you will inevitably find a way to twist the evidence to fit your preconceived view, even if it results in ad hoc fallacies. For example, suppose that ghost hunters go into an abandoned building and detect electromagnetic energy (EM). They will view that as evidence of a supernatural presence, but to those of us who aren’t already convinced that ghosts exist, that energy could be a bad wire, a faulty transformer outside, the cameras, lights,and other equipment being used by the ghost hunters, etc. You see, the explanation that the energy is coming from a ghost is only convincing if you are already convinced that ghosts exist. This is why real science always has to start with the evidence, then form a conclusion. If you set out to prove something, you will always find a way to do it (at least in your mind).

Ghost hunting also suffers a serious paradox which is somewhat unique to it, and which I find highly entertaining. Ghosts are supposed to be paranormal, supernatural, metaphysical, etc. yet ghost hunters try to document their existence by looking for physical clues. This is problematic because, by definition, science is the study of the physical universe. It is inherently incapable of answering questions about the supernatural. So anytime that you are looking for the metaphysical, you are automatically doing pseudoscience, not science.

To put this another way, you cannot prove the existence of the metaphysical by documenting the physical. Let’s say, for example, that a ghost hunter goes into a room and documents an EM field, strange thermal readings, a garbled voice recording, etc. Further, let’s say that this was in an isolated area and somehow the “researcher” had accounted for all known sources of energy. Would he have just succeeded at proving the existence of the supernatural? NO! Because he document physical readings. All that he would have shown was that something happened that we don’t currently understand. You cannot jump from “we don’t understand X” to “X is caused by ghosts.” That’s a logical fallacy known as an argument from ignorance.

This is the hilarious paradox that entertains me to no end: if supernatural ghosts exist, then they are, by definition, untestable using science. Thus, using scientific equipment to look for ghosts is inherently self defeating!

We basically have three possibilities:

Ghosts don’t exist
Supernatural ghosts do exist, but cannot be tested using science
“Ghosts” exist, but are a actually natural, physical phenomena, in which case they can be documented using science

There is no option 4 in which supernatural ghosts exist and can be documented using physical means. That’s just not possible. If ghosts are supernatural, then their existence cannot be demonstrated using science, and conversely, if their existence can be demonstrated using science, then they aren’t supernatural. If you document an unexplained physical clue, then all that you can say is, “we don’t understand this.” You cannot assume that the physical clue was caused by the metaphysical. Indeed, if you think through the history of science, there have been numerous physical phenomena that were attributed to the supernatural before we properly understood them.

Additionally, there is the paradoxical nature of ghosts hunter’s equipment. The equipment that they use to “detect” ghosts is generally designed by them and is based on question begging fallacies. For example, ghost hunters generally argue that ghosts put off an EM field which their equipment can detect, and we can set up their argument like this:

Ghosts emit an EM field
I can detect a ghost’s EM field using this device I built
I went into an abandoned house and detected an EM field
Therefore, a ghost was present

The problem is premises 1 and 2. I would not accept that ghosts put off a detectable EM field unless I was already convinced that ghosts exist. In other words, before you can use an EM field as evidence of a ghost, you have to demonstrate that ghosts put off EM fields, but you can’t demonstrate that ghosts put off EM fields, unless have already demonstrated that ghosts exist! Round and round in a circle we go.

In short, ghost hunting is inherently self defeating because it starts by assuming that ghosts exist and because no amount of physical evidence can ever demonstrate the existence of the metaphysical. To demonstrate the existence of the metaphysical, you would need metaphysical evidence, which science cannot supply for you. So if you want to believe in the supernatural, you are going to have to do exactly that: believe. You cannot, even in concept, support your belief with physical evidence.

This is only tangentially related to my post, but it’s amusing and demonstrates another reason why ghost hunting is pure crap. I’m not sure who made it, so if it is yours please let me know.

Posted in Uncategorized | Tagged ad hoc fallacies, argument from ignorance fallacies, logical fallacies, question begging fallacies | 13 Comments

Ancient knowledge and the test of time

Posted on October 19, 2015 by Fallacy Man

The notion of “ancient knowledge” is a common theme among anti-vaccers and alternative health practitioners. It generally takes one of two basic forms. Either they claim that something is right/effective/safe because our ancestors thought so and they were somehow privy to some “ancient knowledge” that we don’t have access to today, or they argue that a treatment is safe/effective because it has been used for many generations and has stood the “test of time.” Conversely many of them argue that we shouldn’t use vaccines and modern pharmaceuticals because they have not passed this arbitrary test of time. These arguments are, however, appeal to antiquity fallacies. The fact that something is old or has been used for a long time does not in any way shape or form demonstrate that it is safe, effective, etc. So anytime that someone makes one of these arguments, they are committing a logical fallacy and according to the rules of logic, you must reject their argument. Nevertheless, let’s briefly look a bit closer.

These arguments are particularly absurd because the history of science is nothing if not a steady debunking of ancient ideas. Geocentrism, the idea that nature is made of four elements (earth, water, air, fire), alchemy, etc. were all ancient ideas that were later debunked and replaced by science. So the fact that something is ancient clearly does not validate it. To be clear, I’m not suggesting that the fact that many ancient ideas have been refuted means that all ancient ideas are wrong (that would be just as flawed as the logically invalid argument that we shouldn’t trust science because it has been wrong in the past). Rather, I am saying that you cannot assume that something is true/effective/safe just because it is ancient. You have to actually test it scientifically, and you have to accept the results of those tests.

The “test of time” argument is similarly flawed. There are thousands of ancient medical treatments that were used for countless generations before science came along and discredited them. Leeches are a good example. We used them for hundreds of years before we realized that draining a sick person’s blood was a bad idea (note: we do still use leeches medicinally today, but not for the same thing that they were used for historically). Similarly, tobacco was common in Native American medicine and was adopted by European explorers, yet today we know that it is extremely dangerous (Charlton 2004); note: it is a myth that there was once a scientific consensus that smoking was safe. The reality is that the tobacco companies had paid off a handful of scientists, but the scientific consensus was and is that it’s dangerous).

When you think about it, it is, of course, not surprising that many things would be used medicinally for countless generations without anyone realizing that they don’t work. Imagine that in some village, someone gets sick, eats an herb, then gets better just by his/her body healing itself. It will appear that the herb worked because the person took the herb, then got better (this is known as a post hoc ergo propter hoc fallacy). As a result, every time that someone in that village gets that ailment, they will take that herb. Sometimes, it simply won’t work and the person will get worse, but other times, the placebo effect will kick in and the person will get better. Additionally, in many cases, the sick person’s body will simply heal itself, thus giving the appearance that the herb works. Every one of these “success stories” will serve to affirm the villagers’ belief that the herb works, and it will get used from one generation to the next.

The only way to actually tell whether or not the herb works, however, is to test it scientifically with proper controls. In order to know if it actually has healing properties, you have control for confounding factors, and you need to know the background recovery rate (i.e., how many people heal because of the placebo effect, their own body’s healing abilities, etc.). Then, and only then, can you say whether or not the herb works. That is really my fundamental point in all of this. The fact that something is ancient or has been used for many generations does not automatically mean that it works, and making that assumption is logically invalid. Carefully controlled studies are the only way to tell for sure.

Posted in Nature of Science | Tagged alternative medicine, appeal to antiquity fallacies, Bad arguments, logical fallacies, post hoc ergo propter hoc fallacies | Comments Off

100 bad arguments against vaccines

Posted on October 12, 2015 by Fallacy Man

From time to time, I get directed to an article titled “One Hundred Arguments Against Vaccines” which was written by Natural Health Warriors and is nothing more than a Gish Gallop of anti-vaccine tropes. I have been loath to address this article because, quite frankly, I don’t really feel like spending several days debunking this nonsense. Nevertheless, given the frequency with which I encounter this article, I suppose it would be worthwhile to write a rebuttal. So here it is. Those of you who read this blog know that I like to pontificate, and I struggle with brevity, but given that I have 100 arguments to deal with, I will attempt to be terse. Many of these are arguments that I or others have dealt with in more detail elsewhere, so when relevant, I will link to those articles in case you want a more thorough refutation. Also, several of these arguments are nearly identical to each other, so I have grouped those and written a single response to all of them (note: all of the bad arguments are direct quotes from the Natural Health Warriors post [including the caps lock]).

As you read through this, I want you to pay very careful attention to an important difference between the original article and my rebuttal. Namely, the “sources” for the original were almost entirely quack websites like Natural News, Whale.to, Info Wars, etc. Indeed, there were only citations to a few (I think three) peer-reviewed papers in the entire post, and most of them weren’t about vaccines. In contrast, I constantly back up my claims with peer-reviewed studies or statistics from reputable groups like the CDC and WHO. I may direct you to blogs for more detailed explanations, but I always back up factual claims with proper sources. On that note, if you disagree with my arguments, please do not bother to post unless you include references to the peer-reviewed literature. To be blunt, I do not give a crap about your anecdotes, gut feelings, opinions, or “hours of research.” Unless you can back up your position with properly conducted studies, your position is invalid.

Bad Argument #1). “NO vaccine is 100% safe.”
True…but neither is measles, polio, rubella, etc. Vaccines are a basic exercise in risk assessment. They have been tested over and over again, and their risks are extremely small. Conversely, the consequences of the diseases that they prevent are horrible. For example, Clemens et al. (1988) found that the introduction of the measles vaccine reduce death rates by 57%. In short, vaccines are far safer than the diseases that they prevent. For more details (and sources) see this post.

Bad Argument #2). “NO vaccine is 100% effective.”
I could say the same thing about seat belts, car seats, condoms, helmets, parachutes, sunscreen, etc. The fact that something isn’t 100% effective clearly doesn’t mean that we shouldn’t use it (i.e., this argument is logically inconsistent). Also, do you know what is 0% effective? Not vaccinating! (more details here).

Bad Argument #3). “ALL vaccines have severe life-threatening side-effects. Any ‘immunity’ gained from a vaccine is short term only.”
Again, life threatening side-effects from vaccines are extremely, extremely rare. For example, life threatening responses to the MMR vaccine occur in roughly 1 in 1,000,000 cases, and they are usually allergic reactions, which can be treated instantly since you are already at a medical facility. So deaths from vaccines are almost unheard of. In contrast, measles kills 1 out of every 1,000 people that it infects, resulting in thousands of deaths every year. Further, for some vaccines, immunity does last for a long time (Hammarlund et al. 2003; Jokinen et al. 2007), and even when it is short lived, vaccines are still better than having no protection at all. Further, in some cases natural immunity also isn’t life long (Wendelboe et al. 2005).

Bad Argument #4). “There are no long term studies that have been done on the effects of vaccination.”
Define “long term”? Is “long term” 5 years? 10 years? 20 years? Without a clear definition of “long term” this criticism is vague to the point of uselessness. There have been several studies that have looked at vaccine effects over multiple years (Idbal et al. 2013; Ferris et al. 2014; Vincenzo et al. 2014), but “long-term” really needs to be defined beforehand. Also, realize that studies over several decades are inherently problematic because it becomes almost impossible to control all of the variables. Finally, the fact that there aren’t any studies over a 30 year period in no way shape or form justifies that conclusion that vaccines are dangerous (that would be an argument from ignorance fallacy).

Bad Argument #5). “Vaccine safety trials are only carried out on healthy babies, children and adults yet once approved, they are given to everyone – healthy or not.”
Actually, there are quite a few illnesses and disorders (such as being immunocompromised) that will prevent you from getting a vaccine (please see the CDC recommendations). Further, there are studies that look specifically at how people with various medical conditions respond to vaccines. For example, Kramarz et al. (2000) examined the effects of the flu vaccine on children with asthma.

Bad Argument #6). “Vaccine safety trials are paid for by the very people who make the vaccines, so there is no possibility of the information being unbiased or truthful.”
First, there are many safety trials that were not funded by vaccine manufactures (more details here). Second, let’s not forget that many of the people/sites that oppose vaccines make a lot of money from doing so (including sites that this article cites), so this argument is logically inconsistent (more details here and here). Finally, “no possibility,” really? The fact that someone works for a pharmaceutical company does not automatically mean that they are corrupt.

Bad Argument #7). “Unvaccinated children are much healthier than vaccinated children.”
This one links to a statistically invalid, self-reported survey. Basically, they polled their audience of anti-vaxxers and asked them to rate their children’s health, and (big surprise) they said that children without vaccines were healthier. This survey is completely illegitimate. It lacks all of the proper controls and randomizations that would be necessary for it to be valid. It is no different than polling people as they exit Whole Foods and asking them to rate their health when they eat organic vs. non-organic food. Of course they are going to say that they feel better when eating organic, that’s why they are shopping there! In contrast, a properly conducted, peer-reviewed study (Schmitz et al. 2011) compared the health of vaccinated and unvaccinated children, and the only difference was that unvaccinated children had vaccine preventable diseases significantly more often than vaccinated children. Further, Grabenhenrich et al. (2014) found that asthma rates are actually lower among the vaccinated. So in reality, vaccinated children are much healthier.

vaccines work US measles annual infection rates and death rates

US measles infection and mortality rates prior to and following the introduction of the measles vaccine. Notice that although death rates had decreased prior to vaccines, infection rates had not (sources are available at the end of this post).

Bad Argument #8). “Vaccination is NOT responsible for the decline in infectious diseases.”
No, actually it is. I dealt with this argument in detail here, but in short death rates had declined prior to vaccines, but actual infection rates had not. Further, numerous studies have found that disease rates declined sharply following the introduction of vaccines (Clemens et al. 1988; Adgebola et al. 2005; Richardson et al. 2010), and diseases have a nasty habit of returning when vaccine rates drop (Antona et al. 2013; Knol et al. 2013).

Bad Argument #9). “The polio vaccine of the 1950’s and 60’s was contaminated by the SV40 virus which is now confirmed to have caused cancer in many people who had received the vaccine. New viruses are being discovered all the time, so it’s a matter of Russian roulette on when such a virus will sneak into another vaccine.”
This argument boils down to, “the medical technologies of the 1950’s and 60’s were inadequate, therefore the medical technologies today are.” That is just silly. Standards and techniques have come a long way since then. This argument is like saying, “the first plane only flew a few feet, therefore modern transcontinental flights are dangerous.” Also, SV40 doesn’t cause cancer (you can find a lengthy explanation and sources here).

Bad Argument #10).“The cells of an aborted human fetus was used to make the rubella vaccine, which is part of the MMR vaccine.”
This is an appeal to emotion fallacy. Also, just to be clear, there are no aborted cells in the vaccines, and no new fetuses are being aborted for vaccines. The cells are simply used to culture the virus (details here).

insulin GMO vaccine chemistry scary ingredients

Image via Refutations to Anti-Vaccine Memes

Bad Argument #11). “Cow cells, monkey cells and chick embryo cells are all found in various vaccines – how can anyone really know the long term effects of injecting this foreign DNA into a 6 week old baby’s body?”
This is both an appeal to emotion fallacy and an argument from ignorance fallacy. Also, none of those things are actually in the vaccines. Some vaccines contain cell proteins, but they do not contain the cells themselves (here’s a list of vaccine ingredients). More importantly, there is no reason to think that these cell proteins are dangerous, especially when thousands of studies all say that vaccines are safe. Also, realize that chemically, the DNA of all organisms is the same (it’s all deoxyribonucleic acid), and the foreign DNA isn’t going to get incorporated into your child’s genetic code (that only happens in comic books; more details here).

Bad Argument #12). “Add some heavy metals, antibiotics and preservatives and you have a toxic cocktail called a vaccine.”
This is yet another appeal to emotion fallacy (are you detecting a theme here?). Further, it ignores the fact that the dose makes the poison. The “toxic” chemicals in vaccines are present in extremely low doses, and they are perfectly safe at those concentrations (details here).

Bad Argument #13). “Oh, and don’t forget to add some GMO’s to the above as well!”
(sigh) Again, this is an appeal to emotion fallacy. Just because something sounds gross doesn’t mean that it is actually bad. You have to actually provide evidence that GMOs in vaccines are dangerous. Also, don’t forget that the life-saving drug known as insulin also comes from a GMO.

Bad Argument #14). “PHARMACEUTICAL COMPANIES CANNOT BE TRUSTED! They have proven over and over again that they are only in it for the money.”
This is a guilt by association fallacy (i.e., whether or not the companies are ethical has no bearing on whether or not vaccines work). Look, no one is saying that pharmaceutical companies are angelic, benevolent entities that are trying to bring about world peace and ensure that everyone has a unicorn for a pet. They are for profit companies which, by definition, means that their primary goal is money. I’m not contesting that. Also, like essentially all big companies, they will behave unethically for the sake of money, but accepting vaccines isn’t about trusting pharmaceutical companies, it’s about trusting science, and the scientific evidence says that vaccines are safe and effective. Also, please note that there are plenty of vaccine studies that aren’t affiliated with pharmaceutical companies, and vaccines aren’t actually worth that much to pharmaceutical companies (details here).

Bad Argument #15). “An Italian court has ruled that MMR was the cause of autism in this man’s case.”
Your point is…? Judges and lawyers aren’t science experts, and even if they were, the fact that they said that the vaccine caused the autism does not prove that the vaccine caused the autism. This is a blatant inappropriate appeal to authority fallacy. Further, this ruling was later overturned. The “link” between autism and vaccines has been tested dozens of times. We have conducted a massive meta-analysis with over 1.2 million children (Taylor et al. 2014); we have looked specifically at children who are at a high risk of autism (Jain et al. 2015); we have even examined how vaccines affect the brains of rhesus macaques (Gadad et al. 2015), and we have always gotten the same result: vaccines do not cause autism. If you still think that vaccines cause autism, then you are willfully ignorant of reality (I discussed the literature at length here and explained why there are so many anecdotes of autism and vaccines co-occurring here).

Note: none of the three studies that I cited were funded by pharmaceutical companies, in fact, the monkey study was funded by anti-vaccers! Several of the authors of Jain et al. 2015 do work for the UnitedHealth Group and its subsidiaries, but they are not involved in the manufacturing of vaccines).

Bad Argument #16). “In New Zealand a fully vaccinated child in 1961 would have received 12 vaccines for four diseases (four jabs and three sips) up until the age of five. The current vaccine schedule includes 11 injections for 10 diseases by age four. This will continue to increase as the pharmaceutical companies realize they can make more money if they inject our children with more vaccines.”
First, let’s do some basic math. Twelve divide by 4 is 3, and 11 divided by 10 is 1.1. So in 1961, it took 3 vaccines per disease, whereas now it takes 1.1 vaccines per disease (assuming that their numbers are even true, I didn’t check). In other words, the number of vaccines per disease is decreasing. If this was truly all about the money, we would expect the exact opposite, there should be more injections per vaccine, not fewer. Further, the reason that there are more injections today is because today children are protected against more diseases, which is a good thing! This argument is no different from complaining that cars today have more airbags than they did in the 60s!

Bad Argument #17). “Herd immunity by means of vaccination is a LIE the pharmaceutical companies use to make parents feel bad for not vaccinating their children.”
No it’s not., It is an empirical fact which can easily be calculated and simulated (see here, here, and here), and has been experimentally demonstrated numerous times (Monto et al. 1970; Rudenko et al. 1993; Hurwitz et al. 2000; Reichert et al. 2001; Ramsay et al. 2003).

Bad Argument #18).”Vaccines are regularly being withdrawn from the market due to adverse reactions.”
To check the facts on this, I went straight to the FDA website and looked at the vaccine recalls over the past 5 years. There have only been nine, and all of them were for particular batches, not the entire vaccine. In fact, none of them were because the vaccine itself had been found to be dangerous. One was simply that there was an error on the insert package, another was that some vials were cracked, and a third was that the batch had been shipped to the wrong country. Most of the recalls were because the company had preemptively recalled the batch because that particular batch tested below their standards (no adverse effects had been document). In fact, only two of the recalls directly mentioned potential adverse health effects, and both of those were because of problems with the vials, not because of problems with the vaccines themselves. So, rather than showing that vaccines are dangerous, these recalls show an incredibly high level of quality control. Finally, let’s not forget that any product that is made on such an enormous scale is occasionally going to have defects, but the defects are extraordinarily rare compared to the volume that is being produced.

$anti-vaccer bad math, antivaxxer, most people who get a disease where vaccinated against it$

Image via Pro-Vaccine Shills for Big Pharma, the Illumanati, Reptilians, and the NWO

Bad Argument #19). “In New Zealand, 65% of people who contracted whooping cough in 2012 were vaccinated.”
Bad Argument #20). “Most children who catch measles were already vaccinated.”
Anti-vaccers apparently suck at math. It is true, that in raw numbers the majority of infected people were vaccinated, but that is because the majority of the population was vaccinated. So, when you look at the actual percentages (i.e., infection rates), you find that the unvaccinated have far higher disease rates than the vaccinated (I explained the math in more detail here).

Bad Argument #21). “Even ex-vaccine developers are coming out and exposing the lies that the vaccination industry is based on.”
This links to an “interview” with someone who claims to be an ex-vaccine developer (pseudonym: Dr. Mark Randall), but the interview contains all the usual anti-vaccine tropes, as well as clearly unfactual statements, and numerous conspiracy theories (such as the WHO being involved in depopulation efforts). Finally, realize we are given no actual information about this person. We don’t know who he is, where he worked, what his credentials are, or if he actually even exists. We are just supposed to take the interviewer’s word for everything, but the interviewer is not a credible, well-respected journalist, he (Jon Rappaport) is a conspiracy theorist who also writes posts about mind control, HIV conspiracies, etc. In other words, there is absolutely no reason to trust anything in this “interview.” Extraordinary claims require extraordinary evidence, and this just doesn’t cut it.

Bad Argument #22). “The swine flu vaccine caused an increase of narcolepsy cases in children recently.”
For once, the claim is actually true, but it’s not nearly as bad as it sounds. First, this was a regional phenomena (not a global one) and appears to be an interaction between the vaccine and an underlying genetic issue. Second, I reiterate that vaccines do admittedly have side effects (as do all real medical treatments), but they are very rare (see #1). In this case, the three estimated rates of vaccine induced narcolepsy were 1 in 12,000, 3 in 100,000 and 1 in 100,000, all three of which are extremely low.

Bad Argument #23). “At least one death in NZ has been linked to the Gardasil vaccine.”
Bad Argument #24). “…and four more deaths from Gardasil in India.”
This is a post hoc ergo propter hoc fallacy. Just because A happened before B does not mean that A caused B, so the fact that someone died after taking the vaccine does not prove that the vaccine killed them. For the NZ case, this person received the vaccine and died several months later, resulting in the mother being convinced that it was the vaccine, but there is absolutely no way to prove that. In fact, the very article that this post cited contains several other completely plausible explanations for the death.

The article that was cited for the India deaths was more vague, making it hard to refute directly, but I did find this report of seven deaths following Gardasil in India (I presume that the four deaths being discussed are included in those seven), and, importantly, none of those deaths appear to be related to the vaccine. “One girl drowned in a quarry; another died from a snake bite; two committed suicide by ingesting pesticides; and one died from complications of malaria.” So unless you think that the vaccine caused a snake bite, that leaves just two deaths, and the Indian Council of Medical Research concluded that neither of them were caused by the vaccine.

Bad Argument #25). “Homeless people died after being paid £1-2 to participate in a vaccine trial.”
This claim included a link to an article in the Telegraph, and I have been able to find very little information beyond what is in that article. From what I have found, it does appear that there may have been some unethical practices by certain people involved, but that does not prove that all of the scientists/doctors involved in vaccine testing are unethical (that would be a hasty generalization fallacy/guilt by association fallacy). Further, the guilty parties were caught and dealt with, and the study was stopped. Also, it is not clear that the people who died actually died as a result of the vaccine (they may have died for completely different reasons). Finally, this was an experimental trial of a new vaccine, so it doesn’t provide evidence that the vaccines that passed their safety trials are dangerous.

Bad Argument #26). “Since the 1980’s, vaccine manufacturers in the USA have been protected from lawsuits following vaccine injury.”
First, what’s your point? What exactly do you think this proves? Second, you cannot sue them directly, but you can get money through the National Vaccine Injury Compensation Program (NVICP), which is essentially a no fault system that does not require proof that the vaccine was responsible. In other words, you can get money for essentially any potentially plausible claim of vaccine injury even if the vaccine was in no way at fault (more details about the NVICP here and here).

Bad Argument #27). “Vaccination is being used to REDUCE fertility and reduce the worldwide population.”
The logic of this one is so bad it makes my soul cry. Here’s the deal: various vaccine manufacturers have experimented with birth control vaccines (i.e., a vaccine which is designed solely to prevent pregnancy), just as they have experimented with pills, IUDs, and other forms of contraceptives. This does not mean that the vaccines that you receive reduce fertility, nor does it mean that companies are trying to reduce the world’s population. This argument is no different from saying, “the same company makes birth control pills and aspirin, therefore aspirin reduces fertility and is a secret plot to depopulate the planet” (here is their “source“).

Bad Argument #28). “The pro-vaccine movement openly admits that it is willing to sacrifice some lives in order to ‘save’ many. I’m not willing to risk that my child is that one that will get sacrificed due to vaccine damage.”
First, the risk to your child is extremely minimal. No one is asking your kid to be a tribute for District 12. Second, if everyone was as selfish as you, then disease rates would skyrocket and the risk to your child would be much higher than it currently is. Further, let’s not forget that your child is much safer with a vaccine than without it. So this argument is both selfish and ignorant (see Gangarosa et al. 1998; Hahne et al. 2009; Antona et al. 2013; Knol et al. 2013 for the consequences of this type of selfishness, and see this post for why anti-vaccers are actually at fault for outbreaks).

Epic win kid meme vaccines I was fully vaccinated millions of people perfectly fine

If we are going to use anecdotes, I have a few million of them for you. Image created by Melissa Miller.

Bad Argument #29). “Ian’s story.”
Bad Argument #30). “Stephanie’s story.”
Personal anecdotes are completely and totally worthless for establishing causation. Again, the fact that someone died months, weeks, or even days after a vaccination does not prove that the vaccine was the cause (i.e., these are post hoc ergo propter hoc fallacies). Further, if we are going to allow personal anecdotes, then I can easily counter stories like these with the stories of me, my three siblings, my wife, my wife’s brother, all nine of my cousins, and all of my friends, none of whom have had serious reactions to vaccines. We have to rely on carefully controlled studies, not anecdotes (details here).

Bad Argument #31). “And I couldn’t resist throwing in some celebrities who do not vaccinate their kids or at least question the effectiveness and safety of vaccines: Mayim Bialik (Amy Farah-Fowler from Big Bang Theory)”
Bad Argument #32). “Jenny McCarthy.”
Bad Argument #33). “Jim Carrey.”
Bad Argument #34). “Rob Schneider.”
Bad Argument #35). “Donald Trump.”
Bad Argument #36). “The legendary Chuck Norris.”
Bad Argument #37). “Even Dr Oz’s wife doesn’t allow him to vaccinate their kids.”
These seven “arguments” are among the most absurd so far. The author of this article didn’t just cite a celebrity as an expert, they actually blatantly argued that you shouldn’t vaccinate because these celebrates say not to (remember the title of this article is “100 Arguments Against Vaccines”)! This is possibly the most flagrant inappropriate appeal to authority fallacy that I have ever seen (and believe me, I’ve seen a lot of appeals to authority). Who gives a crap what a bunch of celebrates think? The fact that they are famous doesn’t mean that we should we listen to their uneducated opinions. Finally, if we are going to play the celebrity card, I’m going to go with what Stephen Colbert had to say (to be clear, I’m not actually saying you should accept vaccines because Colbert does, it’s just a good quote, and it once again demonstrates that anti-vaxxers use inconsistent logic).

Bad Argument #38). “The medical profession still has no clue how the immune system really works, let alone understand the fragile immune system of a 6-week old baby! Do you really think it’s a wise idea to inject foreign DNA, heavy metals, antibiotics, GMO’s, preservatives, etc. into something if you don’t know how it works.”
That’s odd, because I could have sworn that when I took immunology a few years ago, I had to memorize exactly how the immune system works. In fact, I still have the sizable textbook from that course and it explains the immune system in excruciating detail. Not to mention the fact that literally hundreds of thousands of papers have been published about the immune system. So this claim makes no sense whatsoever. Also, it concludes with an appeal to emotion fallacy (see #10-13).

Bad Argument #39). “Vaccines are NOT ‘free,’ like the Ministry of Health, Pharmaceutical companies, doctors, etc. would have us believe. They cost the NZ tax payer millions! Oh, and we also have to pay for the damage they cause.”
The way that vaccines are paid for varies greatly from one country to the next, so whether or not this argument applies depends on the country that we are dealing with, but let’s say that it is universally true. The argument then becomes, “vaccines are bad because they cost money.” Really? We shouldn’t use something just because it costs money? Did you pay for the computer/phone that you are reading this on? Does that make it bad?

Bad Argument #40). “Pharmaceutical companies cannot be trusted! A video interview with Dr Russell Blaylock on fraudulent vaccine science and ethics – a must watch.”
Bad Argument #41). “Oh and if you need some more evidence of the untrustworthiness of pharmaceutical companies, read the book entitled ‘Diary of a Legal Drug Dealer.'”
These are just repeats of #14 so please see it. Also, Blaylock is hardly a reliable source.

Bad Argument #42). “Vaccination bypasses all the body’s natural defense systems – it’s a totally unnatural process – something we were not designed to have to deal with.”
Apparently the author doesn’t understand how vaccines work because this argument is blatantly untrue. During a “natural” infection, your immune system detects the infectious agent and instructs your body to make cells which can produce antibodies that are specific for that infectious agent. After a vaccination, your immune system detects the infectious agent in the vaccine and instructs your body to make cells which produce antibodies that are specific for that infectious agent. There is only one important difference: in the vaccine, the infectious agent has been modified or killed so that it cannot cause a full blown infection, but the way that your body responds is essentially identical. Also, note that this argument is a blatant appeal to nature fallacy. Even if the claims were true, the fact that something is unnatural does not automatically mean that it is bad.

Bad Argument #43). “Antibiotics are blamed for creating superbugs, yet they are routinely added to vaccines!”
Antibiotics themselves are wonderful. They have saved countless millions of lives. The problem is their overuse. People use them for everything, even when they aren’t really necessary. It is this overuse which causes superbugs to evolve. The superbugs are not going to spontaneously form inside the vaccine. Finally realize that the antibiotics are in there to prevent bacterial contamination. In other words, they are necessary for vaccines to be safe, and if they weren’t there, then vaccines actually would be dangerous.

Bad Argument #44). “Big Pharma keeps on increasing the amount of recommended ‘boosters’ as it’s just such a fabulous way for them to make more money without having to do any extra work.”
Actually, they are recommending boosters because the scientific evidence says that for some diseases, immunity (including natural immunity) wears off over time (Wendelboe et al. 2005). This argument is what we call a question begging fallacy. I would not accept the premise that boosters were all about money unless I had already accepted the conclusion that vaccines were bad (more details and sources on boosters and the longevity of immunity here).

Bad Argument #45). “There is no consideration for a child’s mass when they are given a vaccine – a 6 week old baby is given the same dose as a 5 year old.”
That is because vaccines have been designed to be safe the age at which they are recommended, which means that they are also safe for larger children. In other words, a dose that is designed to be safe for a 6 week old will also be safe for a 5 year old, so there is no reason to adjust the dose.

Bad Argument #46). “Even immunologists admit that vaccines compromise our natural immunity.”
The link for this one was broken, so I don’t know which particular immunologists it was referring to, but regardless, it’s an appeal to authority fallacy. The fact that you found a few immunologists who agree with you doesn’t make you right. Also, there is scientific evidence that artificial immunity is much better than natural immunity (see #47).

Bad Argument #47). “Childhood illnesses actually help to strengthen a child’s immune system.”
They only “strengthen” the immune system in that they prevent you from getting the disease a second time. In other words, this argument boils down to, “you should get measles so that you don’t get measles” (I explained the absurdity of this in more detail here). Further, a recent study (Mina et al. 2015) found that getting a measles infection actually suppresses the immune system for 2-3 years! In other words, it weakens the immune system for up to three years after the initial infection, and during that time you are more likely to get other diseases (more details here).

Bad Argument #48). “The short term immunity that is sometimes gained from vaccination in childhood only means it is much harder for the body to deal with when that immunity has waned and you get the illness as an adult.”
I’m not sure exactly what the author is arguing here, but my guess is that they are arguing that childhood diseases are often worse if you get them as an adult, so it is better to get them as a child. If that is the case, there are several things to note. First, there are these simple, safe, and cheap things called boosters that maintain your immunity even into adulthood. Second, “natural” immunity often isn’t life long as well (details here). Finally, when most people are vaccinated, and herd immunity is high, your chance of getting an infectious disease as an adult is generally extremely low, whereas if you get the disease as a child, your chance of getting a serious complication from it is often very high. For example, for measles infections, 1 in 1,000 will die, 1 in 1,000 will get encephalitis, 1 in 20 will get pneumonia (often requiring hospitalization), and 1 in 20 will get an ear infection (sometimes resulting in permanent deafness).

Bad Argument #49). “http://www.ias.org.nz/wp-content/uploads/ias-brochure-2011.pdf.#sthash.t7LszLZf.dpuf”
This argument was simply a link, and the link is broken.

Bad Argument #50A). “Vaccines are commonly believed to work by producing antibodies. However, a number of researchers have found that the presence of antibodies only indicates that the immune system has come into contact with an antigen. What this means is that we are told of vaccines producing antibodies, which in turn will protect us against disease, is a lie! The presence of antibodies does NOT equal immunity!”
This lengthy ramble makes no sense whatsoever, and it represents a clear lack of understanding about how the immune system works. An antigen is a surface recognition protein that is present on the outer membrane of cells (or bacteria walls). Each type of cell has a specific antigen that your body can recognize (this is how your immune system tells the difference between your cells and a foreign cell). So, when you get an infection, your body learns to identify the antigens of the invading cells, and it produces antibodies for those cells. What vaccines do, is present your body with the antigens without actually giving you the infection. That way your body produces the necessary antibodies without you actually getting sick. So, the mechanism that your immune system uses is identical between vaccines and natural immunity. They both produce antibodies in response to antigens (see $42).

Bad Argument #50B). “We know what the signs and symptoms of the so called “vaccine preventable” diseases (e.g. measles, influenza, pertussis etc.) are. We know the best the treatments (natural or pharmaceutical) for each. However, once vaccinated, possible side effects from the vaccinations (all noted in the insert leaflets) are many and various, and may or may not be successfully dealt with.”
First, this argument ignores that fact that despite our medical knowledge, these diseases often have serious consequences, including death (see #48). Second, we know what the side effects of vaccines are, and serious side effects are extremely rare. For example, for the MMR vaccine, the most common serious side effect is an allergic response, but it only occurs in about 1 in every 1,000,000 cases. Further, it’s going to occur right after the injection, which means that it will happen at a medical facility where treatment is readily available. Finally, the vaccine inserts list every condition that has ever been reported following a vaccine, but that does not mean that the vaccine caused those conditions (that’s a post hoc ergo proter hoc fallacy). You can find a more detailed explanation of the vaccine inserts here.

Bad Argument #51). “This ’60 Minutes’ program was on in July 2011 and looks at two New Zealand children who were brain damaged by the whooping cough vaccine and another who was killed by the Gardasil vaccine. During June 2005 and June 2011, ACC paid out on 449 claims of vaccine damage.”
First, remember again that the fact that an injury follows a vaccine doesn’t automatically mean that the vaccine caused it (this is yet another post hoc ergo proter hoc fallacy). Second, let’s assume that the vaccine was actually responsible. If that’s the case, the vaccine is still the safer option. Vaccines have side effects, no one has ever denied that, but they are safer than the alternative, and two cases of brain damage aren’t nearly as troubling as the numerous deaths that occur without the vaccine. Whooping cough still kills thousands of people annually. In 2008 alone, it killed 195,000 children, and during that same year, the vaccine was estimated to have saved 687,000 lives. So please, stop trying to scare me with your cherry-picked examples. Finally, regarding the claims of vaccine damage, it’s a no fault system and does not constitute evidence that the vaccine actually caused the problem (see #26).

Bad Argument #52). “The Hippocratic Oath states that physicians swear to do no harm – yet vaccines routinely destroy the lives of people right around the world.”
No they freaking don’t! They do, however, dramatically reduce disease and death rates (Clemens et al. 1988; Adgebola et al. 2005; Richardson et al. 2010).

Bad Argument #53). “The polio vaccine actually causes vaccine induced polio paralysis.”
The link for this one goes to a Natural News article, not a legitimate source, and it claims that vaccines are causing non-polio acute flaccid paralysis (NPAFV). This claim is not supported by scientific evidence. It is true that the rates of NPAFV have increased in some areas, but they are caused by various bacteria and viruses, not vaccines (Laxmivandana et al. 2013). Also, to be fair, in extraordinarily rare cases in populations with very low vaccination rates, the virus in the vaccine can mutate to a form that causes polio and can cause paralysis (details here). There have, however, only been a total of 758 cases of this despite millions of vaccinations. Further, let’s not forget that the vaccine has completely eliminated polio from many countries. So overall, the paralysis rates are much, much lower with the vaccine than without it.

Bad Argument #54). “Vaccines accomplishing world depopulation.”
Oh for goodness sake. This claim links to a video where Bill Gates is talking about slowing the human population growth rate and says that vaccines are very useful in accomplishing that; however, slowing the growth rate and depopulating are two entirely different things. As countries get access to technology and proper medical care, people start having fewer children because they don’t need to have as many, since most of them actually survive into adult hood. Vaccines slow the growth rate because when all your children survive, you only need to have one or two; whereas when most of them die in infancy, you need to have a lot. This is a well established fact: in developed countries, people choose to have fewer children. That is completely and totally different from vaccines causing sterility, deaths, etc. Only in the mind of a paranoid conspiracy theorist could Gates’ comment ever be twisted into something sinister.

Bad Argument #55). “GlaxoSmithKline were responsible for the death of 14 babies during illegal vaccine experiments.”
This is a gross misrepresentation. First, the experiment itself was not illegal, but it appears that proper consent was not obtained for all subjects. Importantly, however, the 14 deaths were not associated with the vaccine being tested, because those 14 children were given the placebo! The very article that the author(s) cited says this. So the claim that GlacoSmithKline was responsible for these deaths is an outright lie.

Image via Refutations to Anti-Vaccines Memes

Bad Argument #56). “Even though mercury has been linked to numerous illnesses, it is still routinely used in vaccines.”
First, the mercury in vaccines is ethyl mercury, whereas the toxic mercury is methyl mercury. They are completely different. Further, ever since 2001, ethyl mercury has only been included in some forms of the flu vaccine. Also, the dose makes the poison, and the amount in vaccines is tiny (more details here and here).

Bad Argument #57). “Fully vaccinated doctors get whooping cough – so what’s the point in getting vaccinated.”
Just because something isn’t 100% effective doesn’t mean that it isn’t worth using (see #2). This argument is like saying, “even the people who design air bags have fatal car accidents, so what’s the point of having airbags?”

Bad Argument #58). “Vaccine ingredients can lead to a severe form of kidney disease.”
The dose makes the poison, and the dose in vaccines is tiny. The fact that an ingredient can be harmful in a high dose does not mean that it is harmful in a low dose.

Bad Argument #59). “The whole policy of vaccination is based on money, not on health, safety or anything else that might benefit the human race.”
Actually, pharmaceutical companies make very little from vaccines (details here), and there are many independent scientists and doctors involved (more details here). Further, even if money was the sole goal, that wouldn’t constitute evidence that vaccines aren’t safe and effective. If we were to apply this line of reasoning consistently, then since the whole point of Toyota is to make money, Toyotas must be dangerous.

Bad Argument #60). “Vaccines are the cause for many of the chronic diseases we see these days.”
No they aren’t. Their safety has been rigorously tested over and over again. You cannot make this claim unless you can back it up with properly controlled studies with large sample sizes that were published in reputable peer-reviewed journals. The anecdotes in the link that the author(s) cite just doesn’t cut it.

Bad Argument #61). “Vaccines are used to commit genocide among First Nations people in Canada.”
The “source” for this claim is a “Wholistic Nutrition Counsellor” who was unhappy that Xyolhemeylh Health and Family Services supported vaccines rather than her definition of healthy living, which is, “learning about the connection between body, mind and spirit and allowing the body to heal itself using whole foods, organics, natural medicines.” In other words, she was disgruntled about being asked to recommend science instead of woo. She provides absolutely zero evidence of genocide. The closest that she comes is saying, “I had observed a high incedence [sic] of deaths within the Sto:Lo communities linked to suicides, diabetes, cancer, heart disease,” but I seriously doubt that vaccines cause suicides. So, instead of providing actual evidence of genocide, she simply states that vaccines were being pushed, then gives the usual nonsense arguments about “dangerous toxins” and side effects. In other words, this argument is a question begging fallacy. I wouldn’t believe that the vaccines were being used for genocide unless I was already convinced that the vaccines were dangerous. Finally, one of her cornerstone arguments is that she was instructed not to talk to families about the risks of vaccines. She presents this as evidence of a conspiracy, but that request actually seems completely reasonable given that she would almost certainly have given the families misinformation about “toxins” and encouraged people to rely on diet and exercise rather than vaccines.

Bad Argument #62). “Vaccination is not compulsory in New Zealand or the United States – we have the right to refuse to undergo medical treatment.”
I also have the right to eat nothing but lard until I become Jabba the Hutt, but that doesn’t mean that it’s a good idea. The simple fact that you have the right to do something is not a valid argument for actually doing that thing.

Bad Argument #63). “If you’re religious, then there are plenty of reasons to not vaccinate.”
It would take an entire post to explain the many problems in the various religious arguments, so I will just summarize by saying that if your religion actually says that you should let your children suffer and die rather than using a simple preventative measure, then there is something seriously wrong with your religion. Also, relying on God to protect your child seems rather foolish given the thousands of deaths that occurred prior to vaccines (why didn’t God save those children?).

Bad Argument #64). “More information is becoming available regarding the link to vaccines and Sudden Infant Death Syndrome (SIDS).”
As usual, the link for this claim goes to an anti-vaccine page rather than a legitimate source, and the anti-vaccine page contains various anecdotes, post hoc ergo propter hoc fallacies, correlation fallacies, and shoddy preliminary studies. In contrast, multiple peer-reviewed studies have found that not only do vaccines not increase the risk of SIDS, but is some cases, they may actually lower the risk (Hoffman et al. 1987; Griffin et al. 1988; Mitchell et al. 1995; Fleming et al. 2001; Vennemann et al. 2007a; Vennemann et al. 2007b).

Bad Argument #65). “Most doctors have no idea what ingredients are found in vaccines. If you don’t believe me, ask your doctor at your next visit! Why would you allow your doctor to inject you with something when they do not even know its ingredients?”
Most mechanics don’t know the chemical ingredients in engine oil, so why would you allow them to fill your engine with something when they don’t even know its ingredients? Hopefully you see my point. You don’t have to know every single ingredient to know that something is safe and effective (again, anti-vaccers suck at consistent reasoning). I don’t care if my doctors know the chemical makeup of a vaccine or pharmaceutical, just so long as they know the literature and know the risks and benefits associated with a vaccine/treatment (which they do, btw).

Bad Argument #66). “Only about 1% of serious events are reported to the FDA. That means that 99% of adverse vaccine reactions are not reported.”
First, realize that the 1% number was cherry-picked, and both the opinion paper that the author cited (Kessler 1993) and the study that generated the 1% number (Scott et al. 1987) are rather old and almost certainly don’t reflect the current values. Indeed, a slightly more recent systematic review found that on average 20% of serious events were reported (Hazell and Shakir 2006). Further, those values are for adverse reactions to any drug. You cannot apply a broad generality to something as specific vaccines. Indeed, it seems that the reporting rates for vaccines are much higher (Hazell and Shakir 2006). In fact, vaccine injuries are often over-reported, because many of the cases that get reported to the VAERS are false associations (i.e., an injury followed the vaccine, but the vaccine didn’t actually cause it; see #26).

Bad Argument #67). “The pertussis (whooping cough) bacteria are adapting to the vaccine and mutating, much like antibiotic resistant superbugs, becoming more pronounced.”
First, there is very little scientific evidence to support this argument (Cherry 2012), and the scientific studies that anti-vaccers cite to bolster this claim are always terribly misconstrued. Nevertheless, for sake of argument, let’s assume that pertussis is evolving to “resit” the vaccine. If that were true, the solution would simply be to modify the vaccine. This situation is totally different from antibiotics. You see, antibiotics actually kill bacteria, and the bacteria evolve so that the antibiotics no longer kill them. In contrast, vaccines don’t kill bacteria, viruses, etc. Rather, they teach the immune system how to recognize them. So the only way to mutate such that a vaccine no longer works, would be to mutate a different antigen (surface recognition protein). In other words, if the vaccine teaches the immune system to look out for antigen X, but a bacteria has mutated so that it now has antigen X’, the vaccine will no longer work. Fixing this is, however, extremely simple: just modify the vaccine so that it contains both antigen X and X’.

Bad Argument #68). “30 Years of secret official transcripts show UK Government experts cover up vaccine hazards to sell more vaccines and harm your kids.”
This claim is based on the following report, which claims to have documented 30 years of admittedly disturbing corruption among UK officials. Wading through all of the documents that the report cites would take me days, so instead, let’s just assume that the original report is correct. Even if it is, the claims made by the anti-vaccers are outrageous and unmerited. I see anti-vaccers all over the internet claiming that this report proves that the officials knew that vaccines were dangerous, knew that they didn’t work, tried to stop safety studies, etc. Similarly, the Natural Health Warrior article claims that the officials were trying to “harm your kids.” The reality is that the report simply claimed that officials tried to downplay the side effects of vaccines and prevent parents from knowing about them. There is nothing in the report to indicate that they knew that vaccines didn’t work, were trying to harm children, etc. In fact, the opposite is true. The report says, ”

“Here I present the documentation which appears to show that the JCVI made continuous efforts to withhold critical data on severe adverse reactions and contraindications to vaccinations to both parents and health practitioners in order to reach overall vaccination rates which they deemed were necessary for ‘herd immunity.'”

In other words, the officials were withholding information because they knew that vaccines worked and wanted to make sure that the vaccination rate was high enough to protect everyone. To be clear, people do have the right to know about the side effects of vaccines (even if they are rare and minor, see #1 and 3), but the evidence in the report in no way shape or form justifies that claims being made by anti-vaccers, and it most certainly doesn’t demonstrate or even suggest that vaccines are ineffective or dangerous.

Bad Argument #69). “If you need any further evidence regarding the numerous errors that occur during vaccine manufacturing, storing, administering, etc. then here is a great resource.”
The link for this specific “resource” is broken, but it was something from vaccinetruth.org, which is one of the most counterfactual websites in existence. There is nothing on that website that constitutes a “great resource.” Please see #18 for information on how absurdly tightly regulated the manufacturing process actually is.

Bad Argument #70). “The head of the Center for Disease Control – Julie Gerberding – admits in this interview that vaccines can cause autism-like symptoms. Same difference!”
First, that’s not exactly true. She admitted that vaccines can cause fevers (which we already knew), and in certain cases where a person has other conditions that are already stressing the body (specifically rare mitochondrial disorders), that fever can trigger changes that result in autism-like symptoms. That is extremely different from a broad generalization that “vaccines can cause autism-like symptoms.” Further, the specific case in question is that of Hannah Poling, and it is not at all clear that vaccines were at fault (Doja 2008; Offit 2008; see #15 for more on vaccines and autism).

Second, autism and autism-like symptoms are not in anyway the same thing. Rhinovirus (one of the causes of the common cold) produce hay fever-like symptoms, but that does not mean that Rhinoviruses cause hay fever. This argument commits a logical fallacy known as affirming the consequent.

Note: the “source” for this claim is a Natural News video (“CDC Chief Admits That Vaccines Cause Autism”) that chopped and misrepresented an interview with Gerberding. The key statement occurs at 2:50.

organic food autism corrleation logical fallacy

Correlation does not equal causation. Organic food sales and autism rates are tightly correlated, but that does not mean that organic food causes autism. Image via the Genetic Literacy Project

Bad Argument #71). “Vaccines are the cause for the alarming rise in peanut allergies around the world. When I was a child, I didn’t know a single kid with a peanut allergy in our entire school. These days, peanut-containing products are banned from most school grounds to prevent deadly anaphylactic shock in those who are allergic to peanuts.”
There is no scientific evidence to support the claim that vaccines or their ingredients cause peanut allergies. The fact that vaccines have increased along with the increase in peanut allergies does not mean that vaccines cause peanut allergies. This is a correlation fallacy.

Bad Argument #72). “Yeast is a common ingredient in vaccine manufacturing and has been linked to the rise and cause of asthma in many young children.”
Asthma rates are actually lower among vaccinated children than unvaccinated children (Grabenhenrich et al. 2014).

Bad Argument #73). “Vaccines cause allergies because they clog our lymphatic system and lymph nodes with large protein molecules which have not been adequately broken down by our digestive processes, since vaccines by pass digestion with injections.”
Essentially nothing about this claim is true. Vaccines don’t “clog” our lymphatic system (remember, proteins are microscopic), and although some people are naturally allergic to the ingredients in vaccines, there is no evidence that vaccines cause allergies.

Bad Argument #74). “There was a 4,250% increase in fetal deaths reported to VAERS after the flu vaccine was given to pregnant women.”
First, remember that VAERS is completely self reported and the fact that someone reported a fetal death following a vaccine does not mean that the vaccine was responsible (more details here and here). Therefore, this argument is totally invalid. Second, and more importantly, numerous peer-reviewed studies have examined the effects of flu vaccines on fetal moralities and, guess what, they have all found that flu vaccines do not increase the risk of fetal deaths (Mak et al. 2008; Pasternak et al. 2012a; Fell et al. 2012; Haberg et al. 2013). Similarly, studies have also failed to find increased risks to infants whose mothers were vaccinated during pregnancy (Fell et al. 2012; Pasternak et al. 2012b). You can also find a refutation of the study that produced the 4,250% figure here.

Bad Argument #75). “AIDS was transmitted to the human race via the monkey cells used to make vaccines. I challenge you to listen to this interview with Merck vaccine scientist Dr Maurice Hilleman who admits “I didn’t know we were importing the AIDS virus at the time.”
The claim that the polio vaccine spread AIDs has been thoroughly refuted by scientific tests (Sharp, et al. 2001), including directly testing the vaccine for the presence of HIV (Berry, et al. 2001; Blancou, et al. 2001) and looking at the phylogenetics of the virus and its wild hosts (Rambaut, et al. 2001; Worobey, et al. 2004). You can find more complete summaries of the science here and by Weiss (2001). You can also find an explanation of the actual interview at Respectful Insolence.

Bad Argument #76). “Disease outbreaks still occur in fully and highly vaccinated communities.”
True, but they are often triggered by an unvaccinated person, they are usually easily contained, and they are less common than outbreaks in communities with low vaccine levels. Ultimately, this argument is a sharpshooter fallacy because it ignores the fact that overall, disease rates are much lower among the vaccinated (important sources in #8 and more details here).

Bad Argument #77). “Newly vaccinated individuals are responsible for the spread of disease via ‘shedding’ from live virus vaccines.”
It is important realize that they are “shedding” the inactivated virus that is used in the vaccine. In other words, you cannot get the full disease itself from the shed virus. All you can get is the modified version of the virus that is used in the vaccine. So for most people, this is not a problem, but it can be a problem for people who are immunocompromised, which is why they are encouraged to avoid the feces of those who have been recently vaccinated (which they probably should be doing anyway). To quote the very study that the Natural Health Warriors post cited (Anderson 2008), “Since the risk of vaccine transmission and subsequent vaccine-derived disease with the current vaccines is much less than the risk of wildtype rotavirus disease in immunocompromised contacts, vaccination should be encouraged” (my emphasis).

Bad Argument #78). “Pro-vaccination enthusiasts like to point to pharmaceutical industry sponsored research for evidence that vaccines work. In this interesting article by John Ioannidis, he writes why most published research findings are false.”
This argument is a gross misrepresentation of Ioannidis’s informative work. The main problem that Ioannidis was dealing with was the fact that statistics inevitably produce some false positives, and there is a strong publication bias towards positive results. Thus, many initial studies get published because they got a positive result, but they are later refuted when other studies can’t replicate the results. In other words, this is a problem that mostly arises when there is only one or two papers on a topic. It does not apply to situations like vaccines where you have literally thousands of papers that all agree with each other. Where it does apply, however, is the occasional peer-reviewed anti-vaccine paper that disagrees with all of the other vaccine research. The take home message from Ioannidis is not that we shouldn’t trust science, but that we should critically evaluate papers and look at the literature as a whole rather than focusing on a single study (more details here and here). Finally, many of the studies on vaccines were not sponsored by pharmaceutical companies (details here).

Bad Argument #79). “Those who promote vaccines also happen to be the ones who benefit from it financially”
That is often untrue (details here). Also, let’s not forget that many of the people who oppose vaccines benefit from doing so. For example, Natural News (which the Natural Health Warriors post cites religiously) has a store where they sell you their alternatives to vaccines, so this is a clear case of inconsistent reasoning (details here).

Bad Argument #80). “Some doctors in New Zealand are either intentionally lying about the effectiveness of vaccines, or they are just incredibly ignorant. One such example is Dr John Cameron, who blatantly lied (with a smile on his face) about the flu vaccine on “Campbell Live” earlier this year, and here is the IAS’s response.”
The link to IAS’s response is broken, so I don’t know exactly which claims are being referred to as lies, but the vast majority of the doctor’s claims in the video are backed by solid scientific evidence. The only claims that were suspect were the claim that the flu leads to suicides (I have no idea if that claim is correct or not) and the claim that you can’t even get a mild fever from the flu vaccine (I’m pretty sure that this claim is in fact incorrect); however, neither of those potential errors match the accusation, and even if they did, so what!? Just because one doctor is ignorant doesn’t mean that the flu vaccine doesn’t work (that’s a guilt by association fallacy).

Bad Argument #81). “Courtesy of Dr Goldman: Prior to the universal varicella vaccination program, 95% of adults experienced natural chickenpox (usually as school aged children) These cases were usually benign and resulted in long term immunity. This high percentage of individuals having long term immunity has been compromised by mass vaccination of children which provides at best 70 to 90% immunity that is temporary and of unknown duration, shifting chickenpox to a more vulnerable adult population where chickenpox carries 20 times more risk of death and 15 times more risk of hospitalization compared to children.”
See #48. Also, the overall rates of deaths from chicken pox have dropped markedly following the introduction of the vaccine (Nguyen et al. 2005).

Bad Argument #82). “Many parents have commented that their unvaccinated children are much healthier than their vaccinated children. Here is a blog to read on one such mum’s journey.”
Anecdotes are meaningless. The scientific data show that vaccinated children are healthier (Schmitz et al. 2011). See #7, 29, and 30.

Bad Argument #83). “Why bother getting the flu shot? At best, vaccines are effective against only influenza A and B, which represent only about 10% of all circulating viruses. Therefore, there is a 90% chance you will not even be exposed to an influenza virus.”
How about the fact that during the 1989-1990 outbreak, those who were vaccinated had a 41% lower mortality rate than the unvaccinated (Ahmed et al. 1995)? Influenza is complicated because it constantly evolves. As a result, flu vaccines are not always as effective as most other vaccines, and there is certainly room for improvement, but your odds of getting the flu are generally lower with the vaccine (Osterholm et al. 2012). Also, although not identified in Osterholm’s review, other studies have found that the vaccine is particularly important for the elderly (Nordin et al. 2001).

Bad Argument #84). “Here is a great resource showing cases that were awarded damages by the US Government via the Vaccine Court, which is a federal court of claims for the flu vaccine. Note the number of deaths, in just 2012.”
See #26. Court rulings do not constitute evidence that vaccines are dangerous (judges aren’t doctors or scientists).

Bad Argument #85). “If you’re not yet aware of the lies and scare mongering surrounding the 2006 Bird Flu saga, then start your journey here.”
Seventy-nine deaths across nine countries certainly sounds like cause for concern to me. More importantly, we didn’t have a vaccine for the bird flu in 2006, and even now we are still in the trial stages, so how exactly is this argument about vaccines?

Bad Argument #86). “Oh and don’t forget the Swine Flu hype! History is always repeating itself, so be prepared for more Big Pharma induced scare mongering in the future – with matching vaccines to “save us all” of course!”
Each year in the US alone influenza causes anywhere from 3,300 to 48,600 deaths annually (CDC 2010)! That’s not fear-mongering, that’s a simple fact. The callousness of the anti-vaccine movement astounds me.

Bad Argument #87). “Hassle the Ozzies all you like, but at least they are waking up to the dangers of vaccination, with a 600% increase in the number of parents refusing to vaccinate.”
This is an appeal to popularity fallacy.

Bad Argument #88). “Gardasil contains polysorbate 80, also known as Tween 80, which has been linked to infertility in mice.”
The dose makes the poison. See #10-13.

Bad Argument #89). “An eye-opening report from the International Medical Council on Vaccination.”
Despite its scientific sounding name, the IMCV is a quack group devoted to anti-vaccine ideology. So it’s not a legitimate source. Also, the report in question is full of the typical anti-vaccine drivel that I have spent this entire post refuting.

Bad Argument #90). “More deaths following the MMR vaccine.”
This is yet another post hoc ergo propter hoc fallacy. The fact that an infant died shortly after receiving a vaccine does not mean that the vaccine was responsible. See #23, 24, 29, 30 and 50B.

Bad Argument #91). “This brave Queensland Police officer speaks out about vaccine damaged children being written off as cases of Sudden Infant Death Syndrome (SIDS). If only we had more people brave enough to come out and speak the truth about vaccines.”
This isn’t actually an argument, so there is nothing for me to say here (except of course that in some cases vaccines may actually reduce the risk of SIDS [see sources in #64]).

Bad Argument #92). “A University of Pittsburgh study showed that monkeys developed autism-like reactions when given the same vaccines as children.”
That study (Hewitson et al. 2010) was a preliminary pilot study (the term “pilot study” was literally in its title). The full study with a complete sample size just came out and, as often is the case in science, the preliminary results were wrong. There were no differences between vaccinated and unvaccinated monkeys (Gadad et al. 2015; more details here).

Bad Argument #93). “It’s all about the money, honey.”
No, it’s not, and even if it was, that wouldn’t mean that vaccines don’t work/aren’t safe. See #6, 14, and 59.

Bad Argument #94). “If you’d like some real-life stories of vaccine reactions, you’ll find them on this Facebook page.”
Good grief, how many times do I have to say this: anecdotes are meaningless; only peer-reviewed studies matter! See #7, 29, 30, and 82.

Bad Argument #95). “Serious neurologic, thromboembolic, and autoimmune complications have been reported in patients who received human papillomavirus (HPV) vaccines.”
First, vaccines do admittedly have side effects but serious complications are rare (see #2 and 3). In this case, however, a large cohort study failed to find evidence that the HPV vaccine causes serious neurological, thromboembolic, or autoimmune complications (Arnheim-Dahlstrom et al. 2013).

Bad Argument #96). “And if you’d like just a little more evidence of the fraudulent activities of Big Pharma, have a read of this.”
Once again, I trust the science not the companies who benefit from it. See #14.

Bad Argument #97). “Dr Janet tells us the real reasons behind why doctors push vaccines.”
Perhaps it’s because they actually care about their patients, and (unlike anti-vaccers) they are scientifically literate and realize that literally thousands of studies have shown that vaccines are safe and effective…literally thousands!

Bad Argument #98). “This resource lists some great books about vaccination, with plenty of reasons to not vaccinate.”
Again, the scientific evidence clearly shows that vaccines are safe and effective. Books, blogs, and anecdotes are irrelevant. Until you can back up your position with peer-reviewed literature, you don’t have an argument.

Bad Argument #99). “Information on the link between diabetes and vaccination.”
What a surprise, they cited more anecdotes and post hoc ergo propter hoc fallacies. Unsurprisingly, the actual studies have found that vaccines do not increase the risk of developing diabetes (Jefferson and Demicheli. 1998; DeStefano et al. 2001).

Bad Argument #100). “And argument number 100 to NOT vaccinate: listen to your gut instinct.”
This argument beautifully sums up the entire anti-vaccine movement. It is a bold statement that boils down to, “screw the facts, I know I’m right and you’ll never convince me otherwise.” The anti-vaccine movement has absolutely nothing to do with facts, evidence, or legitimate concerns. It is all about fear, assumptions, a herd mentality, and “mommy instincts.” As should be very clear by this point, the scientific evidence is overwhelmingly on the side of vaccines. All throughout this post, I have cited study after study that has shown that vaccines are safe and effective, but those studies don’t matter to anti-vaccers. They have decided that vaccines are dangerous and no amount of evidence will ever convince them otherwise. This is why their position is so laughably absurd.

P.S. my gut tells me that if you honestly think that your gut is a more reliable source of scientific information than thousands of peer-reviewed studies, then you’re an idiot. I generally try very hard to avoid calling people “idiots” on this blog (believe me it is really hard sometimes), but in this case there is no other way to accurately describe the situation. Trusting your gut instead of scientific evidence is an inexcusable level of willful ignorance that can only be described as stupidity. It’s one thing to be uniformed but willing to learn. It is something else entirely to refuse to listen to contrary evidence and insist that you are right even though you are clearly wrong.

Note: All links to scientifically inaccurate websites are redirected through donotlink.com to avoid boosting those websites’ ratings.

Note: I have made several updates to this article to include additional sources (or in some cases clarifications) that were recommended by readers. Many thanks to everyone who has made these suggestions.

Peer-reviewed sources
I apologize that some of these are behind paywalls. You may be able to get copies through your library or by simply emailing the authors.

Adegbola et al. 2005. Elimination of Haemophilus influenzae type b (Hib) disease from The Gambia after the introduction of routine immunisation with a Hib conjugate vaccine: a prospective study. Lancet 366:144–150.
Ahmed et al. 1995. Reduction in mortality associated with influenza vaccine during the 1989–90 epidemic. Lancet 346:591–595.
Anderson. 2008. Rotovirus vaccines: viral shedding and risk of transmission. Lancet Infectious Disease 8:642–649.
Anjali et al. 2015. Autism occurrence by MMR vaccine status among US children with older siblings with and without autism. Journal of the American Medical Association 313: 1534–1540.
Antona et al. 2013. Measles elimination efforts and 2008–2011 outbreak, France. Emerging Infectious Diseases 19:357.
Arnheim-Dahlstrom et al. 2013. Autoimmune, neurological, and venous thromboembolic adverse events after immunisation of adolescent girls with quadrivalent human papillomavirus vaccine in Denmark and Sweden: cohort study. BMJ 347:f5906
Berry, et al. 2001. Vaccine safety: analysis of oral polio vaccine CHAT stocks. Nature 410:1046–1047.
Blancou, et al. 2001. Polio vaccine samples not linked to AIDs. Nature 410:1045–1046.
Cherry. 2012. Epidemic pertussis in 2012—The resurgence of vaccine preventable disease. 367:785–787.
DeStefano et al. 2001. Childhood vaccinations, vaccination timing, and risk of type 1 diabetes mellitus. Pediatrics 108:e112.
Doja. 2008. Genetics and the myth of vaccine encephalopathy. Paediatrics and Child Health 13:597–599.
Fell et al. 2012. H1N1 influenza vaccination during pregnancy and fetal and neonatal outcomes. American Journal of Public Health 102:e33–e40.
Ferris et al. 2014. Long-term study of quadrivalent human papillomavirus vaccine. Pediatrics 134: e657-665.
Fleming et al. 2001. The UK accelerated immunization programme and sudden unexpected death in infancy: case-control study. BMJ 322:822.
Gadad et al. 2015. Administration of thimerosal-containing vaccines to infant rhesus macaques does not result in autism-like behavior or neuropathology. Proceedings of the National Academy of Science 112:12498–12503.
Gangarosa et al. 1998. Impact of anti-vaccine movements on pertussis control: the untold story. Lancet 351:356–391.
Grabenhenrich et al. 2014. Early-life determinants of asthma from birth to age 20 years: a German birth cohort study. Journal of Allergy and Clinical Immunology 133:979–988.
Griffin et al. 1988. Risk of sudden infant death syndrome after immunization with the Diphtheria–Tetanus–Pertussis vaccine. New England Journal of Medicine 319:618–623.
Haberg et al. 2013. Risk of fetal death after pandemic influenza infection or vaccination during pregnancy. New England Journal of Medicine 368:33–340.
Hahne et al. 2009. Rubella outbreak in the Netherlands, 2004-2005: high burden of congenital high burden of congenital infection and spread to Canada. Pediatric Infectious Disease Journal 28:795–800.
Hammarlund et al. 2003. Duration of antiviral immunity after smallpox vaccination. Nature Medicine 9:1131–1137.
Hazell and Shakir. 2006. Under-reporting of adverse drug reactions: a systematic review. Drug Safety 29:385–396.
Hewitson et al. 2010. Influence of pediatric vaccines on amygdala growth and opoid ligand binding in rhesus macaque infants: a pilot study. Acta Neurobiological Experimentals 70:147–164.
Hoffman et al. 1987. Diphtheria-Tetanus-Pertussis immunization and sudden infant death: results of the national institute of child health and human development cooperative epidemiological study of sudden infant death syndrome risk factors. Pediatrics 79:598–611.
Hurwitz et al. 2000. Effectiveness of influenza vaccination of day care children in reducing influenza-related morbidity among household contacts. 284: 1677–1682.
Idbal et al. 2013. Number of antigens in early childhood vaccines and neurophsychological outcomes at age 7–10 years. Pharmacoepidemiology and Drug Safety 22:1263–1270.
Ioannidis. 2005. Why most published research findings are false. PLoS Medicine 2:e124.
Jain et al. 2015. Autism occurrence by MMR vaccine status among US children with older siblings with and without autism. Journal of the American Medical Association 313:1534–1540.
Jefferson and Demicheli. 1998. No evidence that vaccines cause insulin dependent diabetes mellitus. Journal of Epidemiology and Community Health 52:674–675.
Jokinen et al. 2007. Cellular immunity to mumps virus in young adults 21 years after measles-mumps-rubella vaccination. Journal of Infectious Diseases 196:861–867.
Kessler. 1999. Introducing MEDWatch: a new approach to reporting medication and device adverse effects and product problems. Journal of the American Medical Association 269:2765–2768.
Knol et al. 2013. Large ongoing measles outbreak in a religious community in the Netherlands since May 2013. Eurosurveillance 18: pii-20580.
Kramarz et al. 2000. Does influenza vaccination exacerbate asthma?: Analysis of a large cohort of children with asthma. Archives of Family Medicine 9:617-623.
Laxmivandana et al. 2013. Characterization of the non-polio enterovirus infections associated with accurate flaccid paralysis in south-western India. PLoS ONE 8:e61650.
Mak et al. 2008. Influenza vaccination in pregnancy: current evidence and selected national policies. Lancet Infectious Diseases 8:44–52.
Mina et al. 2015. Long-term measles-induced immunomodulation increases overall childhood infectious disease mortality. Science 348:694–699.
Mitchell et al. 1995. Immunisation and the sudden infant death syndrome. Archives of Disease in Childhood 73:498–501.
Monto et al. 1970. Modification of an outbreak of influenza in Tecumseh, Michigan by vaccination of schoolchildren. Journal of Infectious Diseases 122:16–25.
Nguyen et al. 2005. Decline in mortality due to Varicella after implementation of Varicella vaccination in the United States. New England Journal of Medicine 352:450–458.
Nordin et al. 2001. Influenza vaccine effectiveness in preventing hospitalizations and deaths in persons 65 years or older in Minnesota, New York, and Oregon: Data from 3 health plans. Journal of Infectious Diseases 184:665–670.
Osterholm et al. 2012. Efficacy and effectiveness of influenza vaccines: a systematic review and meta-analysis. Lancet Infectious Diseases 12:36–44.
Pasternak et al. 2012a. Vaccination against pandemic A/H1N1 2009 influenza in pregnancy and risk of fetal death: cohort study in Denmark. BJM 344:e2794.
Pasternak et al. 2012b. Risk of adverse fetal outcomes following administration of a pandemic influenza A(H1N1) vaccines during pregnancy. Journal of the American Medical Association 308:165–174.
Rambaut, et al. 2001. Human immunodeficiency virus: phylogeny and the origin of HIV-1. Nature 410:1047–1048.
Ramsay et al. 2003. Herd immunity from meningococcal serogroup C conjugate vaccination in England: database analysis. BMJ 7385: 365–366.
Reichert et al. 2001. The Japanese experience with vaccinating schoolchildren against influenza. New England Journal of Medicine 344: 889–896.
Richardson et al. 2010. Effect of Rotovirus vaccination on death from childhood diarrhea in Mexico. New England Journal of Medicine 362:299–305.
Rudenko, et al. 1993. Efficacy of live attenuated and inactivated influenza vaccines in schoolchildren and their unvaccinated contacts in Novgorod, Russia. Journal of Infectious Diseases 168: 881–887.
Sharp, et al. 2001. The origins of acquired immune deficiency syndrome viruses: where and when? Philosophical Transactions of the Royal Society of London B 356:867–876.
Schmitz et al. 2011. Vaccination status and health in children and adolescents. Medicine 108:99–104.
Scott and Rosenbaum. 1987. Rhode Island physicians’ recognition and reporting of adverse drug reactions. Rhode Island Medical Journal 70:311–316.
Taylor et al. 2014. Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies. Vaccine 32: 3623–3629.
Vennemann et al. 2007a. Sudden infant death syndrome: No increased risk after immunisation. Vaccine 25:336–340.
Vennemann et al. 2007a. Do immunisations reduce the risk of SIDS? A meta-analysis. Vaccine 26:4875–4879.
Vincenzo et al. 2014. Long-term efficacy and safety of human papillomavirus vaccination. International Journal of Women’s Health 6:999–1010.
Weiss. 2001. Polio vaccines exonerated. Nature 410:1035:1036
Wendelboe et al. 2005. Duration of immunity against pertussis after natural infection or vaccination. Pediatric Infectious Disease Journal 24:S58–S61.
Worobey, et al. 2004. Origin of AIDS: contaminated polio vaccine theory refuted. Nature 428:820–820.

Posted in Vaccines/Alternative Medicine | Tagged anecdotal evidence, anti vaccine arguments, appeal to authority fallacies, appeal to emotion fallacies, argument from ignorance fallacies, autism, Bad arguments, evaluating evidence, inconsistent reasoning, logical fallacies, peer-reviewed studies, post hoc ergo propter hoc fallacies, Safety | 36 Comments

Evolutionary mechanisms part 2: Simulating evolution

Posted on October 5, 2015 by Fallacy Man

Note: if you tried to use this simulation before 10-Jan-16 there was an issue with it which has now been fixed.

This is not really a blog post as much as a resource. Throughout this series, I am going to use a simulator that I wrote in order to model the different evolutionary mechanisms, and I wanted that simulator to be available for people to play around with. So this post is simply an explanation of how the simulator works and how you can use it. Unfortunately, I’m not computer savvy enough to know how to write a simulator that can be run online, so I wrote it for the statistical program R, which a large portion of scientists use. Therefore, you’ll have to download R, but it is free and easy to do. I have provided both very basic instructions and explanations of how this simulation works, as well as more thorough explanations for those who want the details. The actual code itself is provided at the end .

Note: these explanations are predicated on you already understanding terms like allele frequencies, dominant vs. recessive, phenotype vs. genotype, etc. If those terms are foreign to you, please read part 1 of this series before proceeding.

What the simulation does (simple version)

In short, the simulator takes a population for which there are only two alleles for a particular gene. It then randomly mates them (i.e., there is no sexual selection), and it determines which individuals live and die based on their phenotype. At that point, it either moves onto the next generation using those individuals, or it brings new individuals in from a neighboring population. Next, it breeds those individuals to form a new group of offspring, determines who lives and dies, etc. It continues to do this until it has reached the number of generations that you told it to simulate.

The program is designed to be very versatile, so you get to set the original gene frequencies, the selection pressure (i.e., how strongly phenotype affects survivorship), how many individuals immigrate from a neighboring population, what the allele frequencies are in the neighboring population, how many populations it will simulate, how many generations it will simulate for each population, and what the output will look like (at the end of the simulation it makes a graph of the results, but you can decide exactly what it graphs). That may sound complicated, but its actually pretty simple.

I personally think it’s lots of fun to play with and there are many things you can do with it. For example, you can set the survival probabilities for both alleles to 100% (that way there is no selection) and model how population size affects genetic drift. You can also set a selection differential and see how immigration, population size, the strength of selection, etc. affect the population’s ability to adapt.

How to use the simulation (simple version)

Downloading the simulation
First, you’ll need to download R. Follow this link, then navigate to your country and follow one of the links under it (for the sake of this simulation, it doesn’t really matter which of those links you use). Next, follow the instructions for downloading it (it is totally free and safe). Although not strictly necessary, I strongly recommend that after downloading R, you download RStudio, because it makes things much easier to work with (again, it’s free).

Once you have downloaded and installed R and RStudio, copy the code at the end of this post, paste it into R, and hit enter (note: if you are using RStudio and you pasted into the top box, then use control+a to select everything then hit control+r or control+enter or command+r or command +enter [depending on your version and operating system). This will load the simulation into R.

Note (added 10-Jan-16): Unfortunately, when copying the code from wordpress, something screwy happens to the quote marks. So, after pasting the code into R, use ctrl+f to find and replace all of the quote marks. First, copy this quote mark “ and paste it into the “find” box and simply type a quote mark into the “replace” box. After replacing those, do the same thing but with this quote mark ”. That should solve the problem and let you run the code.

If you don’t already have them installed, you will need to install two R packages to run the program. In RStudio, there should be a tab called “packages” on the right hand side. Click that, then click the button called “install” and tell it to install ggplot2 and reshape2 (it will ask if you want it to make a directory, say yes).

Running the simulation
To actually run the simulation, type evolution() into the lower box and hit enter. This will run the simulation using the default values, and when it is done, a graph should appear on the right side (you can adjust the window to see it better as well as saving it as an image file). Each line is a different population. The x-axis shows generations, and the y-axis shows the percentage of alleles that are dominant in each generation (when on default). You should see that the allele frequencies (i.e., the percent of dominant alleles) changes over time.

This shows an example of the output of the simulation using the default values.

If you want to actually manipulate the parameters of the simulation rather than using the default, you will need to include some numbers after evolution(. Here is a brief explanation of what each number does (everything has default values, so you can modify as many or as few of these as you want).

First number = number of dominant alleles in the starting population (default = 100)
Second number = number of recessive alleles in the starting population (default = 100)
Third number = the average percentage of individuals with a dominant phenotype that will survive to a reproductive age (default = 100)
Fourth number = the average percentage of individuals with a recessive phenotype that will survive to a reproductive age (default = 50)
Fifth number = the number of generations it will simulate for each population (default = 100)
Sixth number = the number of populations that it will simulate (default = 10)
Seventh number = the number of individuals that will immigrate from a neighboring population at the end of each generation (default = 0)
Eighth number = the number of dominant alleles in the neighboring population (default = 10, this number only maters if the seventh number is greater than 0)
Ninth number = the number of recessive alleles in the neighboring population (default = 10, this number only maters if the seventh number is greater than 0)
Final value = this determines the graphical output. If you enter graph=”dom” you will get the results as a graph of the percent of dominant alleles in each generation (this is the default). graph=”rec” will give you a graph of the percent of recessive alleles in each generation. graph=”surv” will give you a graph of the percent of individuals who survived to a reproductive age in each generation (i.e., recruitment).

To use these values, simply type them after evolution( and separate them by comas. For example, evolution(100,90,80,70,60,50,40,30,20,graph=”dom”) would run a simulation with a starting population of 100 dominant alleles and 90 recessive alleles (note: the sum of those two values must be a even number). On average, 80% of dominant phenotypes would survive, and 70% of recessive phenotypes would survive. It would run 60 generations per population, and a total of 50 populations. At the end of each generation, 40 individuals would immigrate from the neighboring population, and the neighboring population would consist of 30 dominant alleles for every 20 recessive alleles (i.e., 60% dominant alleles, 40% recessive alleles). When it was done, you would get a graph showing the percent of dominant alleles at the end of each generation (i.e., after selection).

Because there are default values, you can leave any parameters that you want blank, but if you skip parameters, then you need to include the commas. For example, if all that you cared about was changing the initial allele frequencies to 50 and 90, then you could enter evolution(50,90) and the program would run with 50 dominant alleles, 90 recessive alleles, and everything else set to default. If however, you only wanted to change the survivorship so that dominant phenotype never survives (0) and recessive phenotype always survived (100), you would enter, evolution( , ,0,100). You have to have those first two commas because they tell R which parameters your are actually changing. After you enter those parameters though, it knows the defaults. As a final example, suppose that you wanted the survivorship above, but you wanted to change the final output so that you could see the percent that survived each generation. Do this by entering evolution( , ,0,100, , , , , ,graph=”surv”). Anytime that you skip over a value, you need commas.

Important notes
The initial allele frequencies also determine your starting population size. Each individual has two alleles, so the total number of alleles divided by two will give you the starting population size. This means that the sum of your two allele frequencies must equal a whole number. Breeding occurs randomly and with replacement, so one parent can have more than one offspring.

It is ok if you immigrate more individuals than you have alleles in the neighboring population, because the individuals that immigrate are chosen randomly with replacement. In other words, the simulation views the numbers as ratios not true numbers (e.g., 1 and 9, 10 and 90, 100 and 900, etc, are all identical as far as the program is concerned). This only applies to the neighboring population. The initial population uses true numbers, not ratios.

Playing with the data
If you want to save your results as an excel file that you can play around with, there are several things that you need to do. First, on the top toolbar, click Session then Set working directory then Choose directory then navigate to where you would like to save your data. Next, when you run the simulation, you have to name your output. For example results <- evolution() this will name your results “results.” Once the simulation is done, type write.csv(results,”results.csv”) this will make a .csv file of your results, called “results” and save it in whatever directory you told it to (note: you can name your data and file essentially anything you want, but don’t include spaces and don’t call it “evolution”). When you open the file, the first set of columns shows the percent of dominant alleles at the start of each generation, and the second set of columns shows the percent that survived to a reproductive age in each generation. If you want the percent of recessive alleles, then just subtract the dominants from 100.

Detailed instructions and notes about the simulation

Simulation assumptions and guiding rules

Complete dominance (i.e., homozygous dominant individuals, and heterozygous individuals behave the same way, the recessive allele is only expressed in homozygous recessive individuals)
Discrete non-overlapping generations
The number of offspring produced each generation is constant and always equals the initial parent population size
Mating is random, and individuals can produce more than one offspring
Individuals can reproduce sexually or asexually
Mortality only occurs during recruitment into adulthood
New immigrants are adults. Therefore, they do not undergo selection before reproducing

Simulation paramater details

Function = evolution(number_of_dominant_alleles, number_of_recessive_alleles, dominant_allele_recruitment, recessive_allele_recruitment, number_of_generations, number_of_populations, number_of_immigrants, numb_neighb_dom_alleles, numb_neighb_rec_alleles, graph)

number_of_dominant_alleles = number of dominant alleles in the initial parent population (default = 100)
number_of_recessive_alleles = number of recessive alleles in the initial parent population (default = 100)
Note: number_of_dominant_alleles + number_of_recessive_alleles must equal an even number. That number divided by two = size of the parent population
dominant_allele_recruitment = the probability that an individual with a dominant phenotype will survive to adulthood (default = 100)
recessive_allele_recruitment = the probability that an individual with a recessive phenotype will survive to adulthood (default = 50)
Note: dominant_allele_recruitment and recessive_allele_recruitment range from 0–100. 0 = none survive, 50 = 50% chance of survival for each individual, 100 = all survive, etc. Values must be whole numbers. Recruitment values for dominant and recessive alleles are completely independent of each other.
number_of_generations = the number of generations that will be simulated for each population (default = 100)
number_of_populations = the number of populations that will be simulated (default = 10)
graph = the format of the graph that is produced (default = “dom”).
number_of_immigrants = the number of individuals that immigrate each generation (default = 0)
Note: immigration takes place after selection (i.e., after the formation of the new parent generation). Thus, immigrants are not under selection, but their offspring will be. Immigration does not take place until after the first round of selection.
numb_neighb_dom_alleles = the number of dominant alleles in the neighboring population (default = 10)
numb_neighb_rec_alleles = the number of recessive alleles in the neighboring population (default = 10)
Note: immigrants will be randomly selected from the gene pool of numb_neighb_dom_alleles + numb_neighb_rec_alleles
Note: words must be put in quotes.
“dom” = graph of dominant allele frequencies against generation.
“rec” = graph of recessive allele frequencies against generation
“surv” = graph of survivorship (recruitment) against generation.

The following is an example showing the workflow for a run using the following parameters:

evolution(number_of_dominant_alleles=100, number_of_recessive_alleles=100, dominant_allele_recruitment=100, recessive_allele_recruitment=40, number_of_generations=100, number_of_populations=10, number_of_immigrants=10, numb_neighb_dom_alleles=50, numb_neighb_rec_alleles=100, graph=”dom”)

Note: this code could also simply be entered as evolution(100,100,100,40, 100,10,10,50,100, graph = “dom”)

This sows the basic workflow that the simulation uses.

This shows the basic workflow that the simulation uses.

It will construct a population of 100 individuals (each individual has two alleles, so population size will always = [number_of_dominant_alleles + number_of_recessive_alleles]/2).
It will randomly select 100 sets of two alleles (i.e., individuals).
It will apply the recruitment levels to each individual. In this example, all individuals with a dominant phenotype will survive, but each individual with a recessive phenotype will have a 40% chance of survival. The probabilities are run independently on each individual, so on any given run, you may have slightly more or less than 40% of recessives surviving, but the average survivorship for homozygous recessives will be 40%.
It will randomly select 20 alleles (10 individuals) from the neighboring population, and add them to the population of survivors, thus forming the new parent generation (if there was no immigration, this step would be skipped, and the survivors would simply be the new parent generation).
It will repeat steps 2–4 until it has simulated 100 generations (not including the initial parent generation). Each new run of step 2 begins with the new parent generation formed in step 4.
Once it has complete all 100 generations, it goes back to step 1 and starts a new population. In this case, it will make 10 populations in total.

Warning messages
Depending on the version of R you are using, and whether or not the population goes extinct part way through a simulation, you may get any of the following warning messages. Just ignore them, the simulation will still run correctly. “package ‘ggplot2’ was built under R version 3.0.3”
“package ‘reshape2’ was built under R version 3.0.3″”No id variables; using all as measured variabls””In loop_apply(nc, do.ply): Removed rows containing missing values (geom_path).”

General Notes

If there is no immigration, populations may go extinct part way through the simulation; however, this is less likely than it would be in a real population because the same number of offspring are produced each generation, regardless of the number individuals who survived to form the new parent population. This simplification does not adversely affect the overall patterns, and there are some real populations where the number of offspring produced is highly dependent on the number of competitors, so it is not an entirely unreasonable situation.

If there is immigration, the population make go extinct and recover multiple times during a simulation.

It may seem odd that the program selects alleles from the entire gene pool of the parent population rather than splitting the parents up into homozygous dominants, heterozygotes, and homozygous recessives, however, mathematically, both situations are identical because each allele has a 50% chance of being passed from a given parent.

For example, if your population consisted of 10 homozygous dominant individuals, 20 heterozygotes, and 10 homozygous recessives (total dominant alleles = 30, total recessive alleles = 30), then we would calculate the odds of an allele being dominant as follows:

The odds of having a homozygous dominant parent = 25% ([10/40]*100)
If a homozygous dominant parent is selected, the chance of getting a dominant allele from that parent = 100% ([2/2]*100)
Thus, from the product rule, the overall odds of getting a dominant allele from a homozygous dominant parent = 25% ([0.25*1]*100)
The odds of having a heterozygous parent = 50% ([20/40]*100)
If a heterozygous parent is selected, the chance of getting a dominant allele from that parent = 50% ([1/2]*100)
Thus, from the product rule, the overall odds of getting a dominant allele from a heterozygous parent = 25% ([0.5*0.5]*100)
Finally, via the sum rule, the overall odds of an allele being dominant = 50% (0.25+0.25)

Now, using the algorithm used in the simulation, where alleles are randomly selected from the gene pool without regards for homozygotes or heterozygotes, we find that there are 30 dominant alleles and 30 recessive alleles, so the chance of getting a dominant allele is 50%. So this method produces the same results as the more complicated method of including homozygotes and heterozygotes.

The actual program

Everything below this is the actual code. Simply copy it and paste into R as described above.

evolution <- function(number_of_dominant_alleles=100, number_of_recessive_alleles=100, dominant_allele_recruitment=100, recessive_allele_recruitment=50, number_of_generations=100, number_of_populations=10, number_of_immigrants=0, numb_neighb_dom_alleles=10, numb_neighb_rec_alleles=10, graph=”dom”){
require(ggplot2)
require(reshape2)

#the following simply makes a nice header row and serves as the source for the .i dataframes
c1 <- as.data.frame(cbind(c(“Parent”,rep(“Generation”,(number_of_generations)))))
c2 <- as.data.frame(cbind(c(“population”,(2:(number_of_generations+1)))))
c1 <- as.data.frame(cbind(c1,c2))
c1 <- as.data.frame(paste(c1[,1],c1[,2]))

perc_dom_alleles.i <- c1
perc_recruitment.i <- c1
for(i in 1:number_of_populations){
pop <- c(rep(1,number_of_dominant_alleles),rep(2,number_of_recessive_alleles))
pop_size <- (length(pop)/2)
prob_dom_recr <- rep(1,dominant_allele_recruitment)
prob_dom_recr <- c(prob_dom_recr,rep(2,100-length(prob_dom_recr)))
prob_rec_recr <- rep(1,recessive_allele_recruitment)
prob_rec_recr <- c(prob_rec_recr,rep(2,100-length(prob_rec_recr)))
neighb_pop <- c(rep(1,numb_neighb_dom_alleles),rep(2,numb_neighb_rec_alleles))

results.i <- as.data.frame(cbind(((number_of_dominant_alleles/(number_of_dominant_alleles+number_of_recessive_alleles))*100),NA))
colnames(results.i) <- c(“% dominant alleles”,”% recruitment”)
for(i in 1:number_of_generations){
gen.i <- NULL
#This loop will construct the population. Each individual came from a random sample of the parent population. Selection was done with replacement. 4 = homozygous dominant, 5 = heterozygous, 6 = homozygous recessive
for(i in 1:pop_size){
ind.i <- sum(sample(pop,2,F))
ind.i <- if(ind.i == 2){ind.i = 5}else{if(ind.i==3){ind.i = 6}else{if(ind.i==4){ind.i=7}}}
gen.i <- c(gen.i,ind.i)}
gen.i <- sort(gen.i)
dom.i <- length(gen.i[gen.i==5])
het.i <- length(gen.i[gen.i==6])
rec.i <- length(gen.i[gen.i==7])
#this calculates the number of homozygous dominant survivors that get recruited into the next generation
dom_surv.i <- sample(prob_dom_recr,dom.i,T)
dom_surv.i <- sum(dom_surv.i[dom_surv.i==1])
#this calculates the number of heterozygous survivors that get recruited into the next generation
het_surv.i <- sample(prob_dom_recr,het.i,T)
het_surv.i <- sum(het_surv.i[het_surv.i==1])
#this calculates the number of homozygous recessive survivors that get recruited into the next generation
rec_surv.i <- sample(prob_rec_recr,rec.i,T)
rec_surv.i <- sum(rec_surv.i[rec_surv.i==1])
#this calculates the percent of dominant alleles in the population after selection (i.e., at the start of the next generation)
perc_dom.i <- (((dom_surv.i*2)+het_surv.i)/(sum(dom_surv.i,het_surv.i,rec_surv.i)*2))*100
#this calculates the recruitment rate (as a percentage of offspring)
perc_surv.i <- ((sum(dom_surv.i,het_surv.i,rec_surv.i))/pop_size)*100
gen_res.i <- cbind(perc_dom.i,perc_surv.i)
colnames(gen_res.i) <- c(“% dominant alleles”,”% recruitment”)
results.i <- as.data.frame(rbind(results.i,gen_res.i))
#this introduce immigrant alleles. This happens after selection and survival/allele estimates. Thus, it only affects the gene pool for the next generation
imm.i <- sample(neighb_pop,(number_of_immigrants*2),T)
pop <- c(rep(1,((dom_surv.i*2)+het_surv.i)),rep(2,((rec_surv.i*2)+het_surv.i)),imm.i)
if((perc_surv.i+number_of_immigrants) == 0){break}}
null_col <- length(results.i[,1])
null_col <- as.data.frame(cbind(c(rep(NaN,(number_of_generations+1)-null_col)),c(rep(NaN,(number_of_generations+1)-null_col))))
colnames(null_col) <- c(“% dominant alleles”,”% recruitment”)
results.i <- as.data.frame(rbind(results.i,null_col))
results.i <- round(results.i,1)
dom_a.i <- as.data.frame(cbind(results.i[,1]))
recru.i <- as.data.frame(cbind(results.i[,2]))
perc_dom_alleles.i <- as.data.frame(cbind(perc_dom_alleles.i,dom_a.i))
perc_recruitment.i <- as.data.frame(cbind(perc_recruitment.i,recru.i))}
results.i <- cbind(perc_dom_alleles.i,perc_recruitment.i)
c1 <- c(“”,rep(“Population”,number_of_populations),””,rep(“Population”,number_of_populations))
c2 <- c(“Generations”,1:number_of_populations,”Generations”,1:number_of_populations)
c1 <- as.data.frame(cbind(c1,c2))
c1 <- (paste(c1[,1],c1[,2]))
colnames(results.i) <- c1

if(graph == “dom”){
g.i <- as.data.frame(results.i[,1:number_of_populations+1])}

if(graph == “rec”){
g.i <- as.data.frame(results.i[,1:number_of_populations+1])
g.i <- 100-g.i}

if(graph == “surv”){
g.i <- as.data.frame(results.i[,(number_of_populations+3):ncol(results.i)])}

g.i <- melt(g.i)
col1 <- as.data.frame(cbind(rep(c(1:(number_of_generations+1)),number_of_populations)))
g.i <- cbind(col1,g.i)
colnames(g.i) <- c(“Generation”,”variable”,”Percent”)
graph <- ggplot() +
geom_line(data = g.i, aes(x = Generation, y = Percent, group = variable), color = “#000099”)

graph <- graph + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank(), axis.line = element_line(colour = “black”))
graph <- graph + scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(limits = c(0, 100))
print(graph)
graph <- results.i
print(graph)}

Posted in Science of Evolution | Tagged evolution, gene flow, genetic drift, mutations, natural selection | 1 Comment

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