How many diseases does it take to map a SNP? Fifteen years on

Ken and I are here in Finland, preparing to teach a week of Logical Reasoning in Human Genetics with Joe Terwilliger and colleagues.  Not statistical methods, not laboratory techniques, not the latest way to analyze sequence data.  Concepts, logical reasoning.

Ken and Joe have been reasoning logically for a long time.  They've taught this course together in many places, and they wrote at least one logically reasoned paper 15 years ago.  That paper was published in Nature Genetics.  That journal shortly afterwards made an editorial policy decision to be the loudspeaker for genetic association studies (GWAS), and would be unlikely in the extreme to publish such a view today.  But Joe often says that it could, and probably should be published again, with very few wording changes.  (He also says that if overhead projectors were still available, he'd give the same talks he gave in 1995, since the issues in human genetics haven't changed.  We have lots more data, but no fundamentally new concepts or insights regarding SNP associations and complex traits.  In fairness, though, he does update his slides -- he adds photos of the latest places he has traveled.  Looking forward to photos of Crimea this week.)

The 2000 paper was called, "How many diseases does it take to map a SNP?"  They began:

There are more than a few parallels between the California gold rush and today's frenetic drive towards linkage disequilibrium (LD) mapping based on single-nucleotide polymorphisms (SNPs). This is fuelled by a faith that the genetic determinants of complex traits are tractable, and that knowledge of genetic variation will materially improve the diagnosis, treatment or prevention of a substantial fraction of cases of the diseases that constitute the major public health burden of industrialized nations. Much of the enthusiasm is based on the hope that the marginal effects of common allelic variants account for a substantial proportion of the population risk for such diseases in a usefully predictive way. A main area of effort has been to develop better molecular and statistical technologies often evaluated by the question: how many SNPs (or other markers) do we need to map genes for complex diseases? We think the question is inappropriately posed, as the problem may be one primarily of biology rather than technology.

Today, emphasis is more on ever larger sample sizes to find rare alleles, since common alleles turned out not to be the magical answer, but the issues are the same.  The problem is biological, rather than one of sample size. And not only do we have at least as much causal complexity due to environmental factors, but to the mix have been added the comparable complex 'genetic' causal factors as epigenetic modification of DNA affecting gene expression, and the potential contributions of highly complex microbiome.

The idea of mapping diseases from SNPs is that markers will be near the disease allele.  But, there are problems with this, as GWAS are successfully showing.

If traits do not strongly predict underlying genotypes, that is, if P(GP|Ph) is small, linkage and LD mapping may have very low power or may not work at all. As an extreme example, one's genotype cannot be reliably determined by merely stepping on the bathroom scale! But even if this could be done, there is a widespread but invalid belief that because something can be mapped (that is, P(GP|Ph) is high), the causal predictive power of the genotype (P(Ph|GP); Fig 1, blue arrow) will also be high. In fact, we have surprisingly little data on this latter topic, which requires extensive sampling from the general population, rather than patients. Note that the opposite can also be untrue—that is, if P(Ph|GP) is high it does not mean P(GP|Ph) will be high, as in genetically heterogeneous mendelian disorders such as retinitis pigmentosa. It is important to note that when we speak of P(Ph|GP) in this context, we speak of the marginal mode of inheritance, which is only valid for consideration of singletons, and relatives will not have independent and identically distributed penetrances (even without assuming epistasis or gene-environment interactions) because the other genetic and environmental factors are also correlated among them! Similar arguments can be made about detectance, P(GP|Ph), which must always be a function of the ascertainment, something that is often overlooked in the literature when investigators make comparisons of power for different study designs

 Figure 1. Schematic model of trait aetiology.
The phenotype under study, Ph, is influenced by diverse genetic, environmental and cultural factors (with interactions indicated in simplified form). Genetic factors may include many loci of small or large effect, G_Pi, and polygenic background. Marker genotypes, Gx, are near to (and hopefully correlated with) genetic factor, G_p, that affects the phenotype. Genetic epidemiology tries to correlate G_x with Ph to localize G_p. Above the diagram, the horizontal lines represent different copies of a chromosome; vertical hash marks show marker loci in and around the gene, G_p, affecting the trait. The red P_i are the chromosomal locations of aetiologically relevant variants, relative to Ph.

Other inconvenient biological issues, mentioned in the paper, include that linkage disequilibrium is stochastic, and this has implications for the use of SNPs in disease mapping, that regulatory rather than protein coding sites often affect disease risk, and these are generally impossible to identify (see below), that late-onset chronic diseases are much more complex than the clearly genetic pediatric disease, that the most effective disease mapping and association studies are done in "selective samples of individuals or families at high risk relative to the average risk in the population, and from populations with unusual histories" (hence, Joe's eclectic and interesting travelogue), etiology tends to be very heterogeneous, phenotype can't predict genotype and vice versa, environmental effects can be significant, but are unpredictable and often impossible to identify, and so on.

Whole genome sequencing will not be a general miracle cure.  Exome sequencing can sometimes find  coding variants that have strong effects because we know how to identify exomes and how to read their code.  But many if not most mapped sites for complex traits, as might be expected, are in regulatory regions.  Yet we are still quite inept at identifying regulatory regions, for many reasons not least having to do with their complexity and fluidity among individuals and populations.  So whole genome sequencing will likely have to be analyzed by using markers, as in GWAS, and that will not automatically show us where key regulatory affects are located or how they work.  If these are too heterogeneous, they'll vary hugely, so that mapping will still face the complexity problem.  Time will tell what transpires.

The problems faced in treating complex diseases as if they were Mendel's peas show, without invoking the term in its faddish sense, that 'complexity' is a subject that needs its own operating framework, a new twenty-first rather than nineteenth—or even twentieth—century genetics.

So, if the data are better and less costly now than 15 years ago, the basic issues haven't changed.  

Populations, individuals and imprecise disease prediction

As Michael Gerson writes in the Washington Post, "Preventable infectious disease is making its return to the developed world, this time by invitation." When anti-vaxxers were few, and they chose not to expose their kids to what they consider toxins, their kids benefited from the herd immunity that resulted from most parents choosing to have their kids vaccinated (or, as Gerson puts it, anti-vaxxers chose to be free-riders).  They could claim there were no costs to their action (or non-action), because as long as they were a small minority, there weren't.

But unlike many complex non-infectious diseases, infectious diseases are very predictable. Once the proportion of a population that is immunized falls below a certain threshold, as determined by the rigorous and empirically tested mathematics of infectious disease, the kids of anti-vaxxers are then sitting ducks for disease once they are exposed, and the disease is then likely to spread even to the immunized population because no vaccine is 100% effective.  And this is happening in the US now with measles.  Anti-vaxxers convinced enough of their neighbors not to vaccinate that they can no longer claim no cost, only benefits to their beliefs.

From Mother Jones, 2014

In theory, herd immunity protects a population from measles when at least 90-95% of the population is vaccinated, so the above map would suggest that even in Oregon, with a rate of non medical immunization exemption over 6%, the disease would be unable to gain a foothold.  But, infectious disease researcher Marcel Salathé who is here at Penn State nicely describes herd immunity here, and suggests that something closer to 100% coverage would actually be required to protect against measles because of pockets of lower vaccination rates, and non-random mixing of the population and so forth.

The science on the safety and efficacy of vaccination is well-established. Vaccines can have side-effects, but it's pretty clear the list doesn't include autism.  (Has anyone estimated the prevalence of autism in anti-vaxxer communities yet?  If they were right that the MMR vaccine causes autism, the rate should be a lot lower in unvaccinated kids by now, no?)  The freedom of choice issue, of whether the state has a right to require individuals to be vaccinated, is a live one, and any conscientious objector to the right of society to make decisions for individuals has to be struggling with this one, given the societal consequences.  This is distinctly not the same as an individual's freedom to decide whether to smoke or to drink jumbo soft drinks because in the case of vaccines, what's good for society is also good for individuals, and vice versa.

Measles virus; Wikipedia (Cynthia S Goldsmith Content Provider, CDC) 

But that's not what interests me particularly here.  What interests me is the interplay between population and individual disease dynamics.  Infectious disease dynamics depend on the proportion of susceptible individuals in the population; too few and the disease dies out, enough and the disease sticks around, cyclically infecting people as, say, the flu, or endemic, as, e.g., venereal diseases.  So, in a very real sense infectious disease happens to a group, in a group, and because of a group, at the same time it's happening to individuals in that group.  But chronic non-infectious diseases (CNIDs) don't work that way.  Chronic non-infectious diseases happen to individuals only, irrespective of what's happening to anyone else in the population (though, see below).

But, what we know about and predict for individuals depends on what we know (or think we know) about a CNID in a population.  Epidemiologists collect data in a group on what may be relevant risk factors, and then statistically estimate their importance and impact. Observations on a single individual don't have the power to allow epidemiologists to determine which risk factors are likely to be important.  That requires repeated observations, on many people.

So, calculations of how likely you are to have a heart attack given your age, body mass index, cholesterol levels and so on are based on population associations between such factors and actual heart attacks.  These are based on past observed experience in many individuals.  As we've written many times before, it's not really clear what 'risk' represents, other than the observed proportion of a population with apparent past exposure to tested risk factors who went on to develop a disease.  But, a lot of people with the same risk factors didn't develop disease, or have a heart attack or whatever, and a lot of people without those risk factors did.  So, risk estimates are population-based statistics that may or may not apply to you -- or anyone individually, really.  They are collective data, and clearly don't explain all risk, or allow precise prediction.

Now, social epidemiologists would say that chronic diseases can be as much a result of population factors as infectious diseases are.  Smoking, obesity, drinking, stress-related diseases are correlated with social class, so in a very real sense, population dynamics can affect risk of CNIDs as well.  So, if we want to explain CNIDs by distal rather than proximal risk factors, population dynamics become important, as with infectious diseases.  But they are still population-level factors -- not every low-income individual is obese or has high blood pressure, and not every overweight individual is poor.  But, everyone with measles has been infected by the measles virus.

The population issue, I think, goes a long way toward explaining why it's so hard to predict chronic disease, genetic or otherwise.  We're forced to infer group statistics to individuals, and that's never going to be precise.

Epidemiology behind a rack of eight-balls?

With some diseases and disorders it's very clear when incidence is rising -- asthma in the 1980's and 90's, obesity since World War II, type 2 diabetes, heart disease.  Which is not to say that it's always clear why (other than what's obviously true, that infectious diseases aren't killing us when we're young, so we age into some chronic disease), but at least it's fairly clear that the number of new cases per population-at-risk (incidence) has risen in a given time period.

With other diseases and disorders, it's not necessarily so clear.  Autism spectrum disorders, attention deficit disorder and attention deficit hyperactivity disorder (ADD and ADHD), anxiety, depression; yes, incidence rates have risen, and sharply, but this may reflect increased knowledge or changing definition of the disorder and thus increased diagnosis rather than a true increase in cases.

Incidence of yet other conditions, like schizophrenia, continues apace, and prevalence (the proportion of the population with the condition) doesn't change much over decades.

Ken and I were talking about this. How do we know if a condition has truly become more common, or if apparent increased incidence reflects other things?  Depression, for example.  The following map indicates that prevalence of depression in the US ranged between 4.8% and 15% in 2006 and 2008.    

Data Source: CDC. Current Depression Among Adults --- United States, 2006 and 2008. MMWR 2010;59(38);1229-1235. (this map includes revised state estimates)

And a 2006 paper in The American Journal of Psychiatry reports an increase in prevalence from 3.3% in 1991-1992 to 7.1% in 2001-2002.  But are we more depressed, or just more frequently diagnosed as depressed?

Incidence of diagnosed ADHD has increased significantly, from "7.8% in 2003 to 9.5% in 2007 and to 11.0% in 2011".  Again, whether this is 'real' or an artifact of changing diagnostics isn't clear.  Incidence of autism increased more than ten-fold in the last 40 years.  Certainly some of this is due to increased ascertainment, and some due to broadening of the definition of the trait.  But, probably not all.

Let's say it's real?
But, let's assume these increased incidence rates for behavioral disorders in fact represent something real. We can dismiss genetic causation per se out of hand, because genes don't change this quickly.  (That is, we, as in Ken and I, since hundreds of millions of dollars have been spent on the genetics of susceptibility to all of these conditions by people hoping to find a simple genetic explanation.)

And perhaps there is a simple explanation, though not a genetic one.  If incidence of a disease changes quickly, that may be an indication that there's a single environmental risk factor that explains the change.  Increased lung cancer rates pinpointed smoking, it has been suggested that decreasing stomach cancer rates may indicate year round availability of fresh fruits and vegetables, lead paint exposure can lead to cognitive disorders in children, of course infectious agents are responsible for many diseases, and so forth.  Just as with single-gene disorders, single environmental factors can have major effects, and environmental epidemiologists spend lots of time looking for them.  They can be very elusive.

If the cause is not genetic per se (it's true that genes are involved in everything, but they aren't the interesting or changeable aspect of these diseases with quickly rising incidence rates), it means that something environmental has changed, and is responsible.  It can mean that a subset of the population has more of a genetic susceptibility to that change than others, and sometimes knowing the genetic risk factor can be useful in prevention.

But this isn't likely to be the rule with most complex chronic conditions, and generally it's a very small subset of the population.  And generally, one might expect that with rapidly rising incidence of a disease that's due to an environmental risk factor, only some genes would be responding to the factor whose prevalence has increased -- that is, that the factor was a substrate for a particular gene or something like that.  That is basically the only seriously justifiable rationale for the very extensive mapping that so many insist on doing, to find the genetic basis of traits that are, by and large, clearly not 'genetic' in the usual sense.

And, for both traits that have had stable incidence patterns and those with very major rapid change, the patterns of complexity of genomic contributors is about the same.  There are individually very rare clear genetic causes, for most traits, that aren't really due to environmental change (e.g., they're present at birth or very early in life), but again, this is not the rule.

So yes, of course, since genes are involved in everything, even clearly environmentally-induced diseases are due to gene environment interaction of a sort, but the evidence even in rapidly increasing disorder shows that focusing on the gene part of that isn't going to help prevent these disorders and diseases.  So let's stick with environment, which is presumably something that can be changed.

It seems that it should be easy to figure out something relatively clear-cut and simple that has changed in the environment to cause our widespread disease of interest.  Another way to put this is to ask if rapid increases in prevalence suggest a single, or 'point' cause, rather than some smear of multiple causes.

Ten percent of smokers develop lung cancer, and we consider smoking to be a hugely damaging single sledgehammer of a causal factor.  And, ten percent of people exposed to Factor-X are developing ADHD or depression, so we should consider X to be hugely damaging, an environmental factor with a large effect.  And thus one might expect Factor-X to be easy to ascertain.  Another bird in the hand for environmental epidemiology.

Except that so often it's not.  We've got little clue about environmental factors associated with, or causing ADHD, or widespread depression, or autism.  Or asthma, or diabetes, or obesity.  In part, probably because many differing traits are subsumed in each of these categories, and there are many pathways to each one of them.  But, even if there's a single cause, it can be difficult to identify.  And perhaps requires making unlikely connections.

Speaking of which, Ken has an idea about ADD and ADHD. I'll let him explain:

'Dys-synchrony'?
One often hears about television and in particular rapidly flashing, constantly moving programming aimed at kids (indeed, at adults as well).  It once may have been ads that did this most intensely, to keep you tuned in and not off to get a bite before the actual program returned. But every program these days, from sports, to scoreboards, to ads, to programs, and also to video games, is a breathless race of succeeding images.

Now, I'll present this idea, and hope perhaps some reader will comment with an answer, or references.  First, video screens run on many cycles per second, depending on how its done and its electric AC frequency.  Some technologies, like old CRTs, refresh the screen by painting from a top corner down to a bottom opposite corner.  Digital screens--I think!--refresh by loading the whole screen basically at once.  I understand from some quick Google checking, that some systems, to make motion less herky-jerky, interleave other images, such as a flash of pure black.  I don't know how fast this happens or how accurate our understanding it.  At least, I cannot easily find the answer on the web. But it may be relevant.

Your retinal cells capture photons in their opsin proteins which are then insensitive for a brief instant (another thing I could not easily find, but which also must be well-known to relevant specialists), and then the opsin is back in business ready to capture another photon.  Nature sends streams of photons to our eyes continuously, and our eyes refresh at their natural rate, which is not (I think) like a whole-screen-at-once process.  How our brains interpret the changing images that reach it in this way is not well-known, as far as I an aware, but that isn't the question.

In our current world, besides the frenetic stream of images from screens like the one you are reading the one I am writing on, the images are synchronized, in one or another form of lock step.  This is not designed to be in lock step with retinal refresh patterns and is far more orchestrated than visions of natural nature.  In other words, I wonder if our brains, evolved to process images that come in an unsynchronized way (or in a way synchronized by the brain) are forced to process images that try to synchronize the brain's reception.  Could this be a kind of dissonant information phenomenon, a 'dys-synchrony', that causes stress or confusion of brain function, and that shows up symptomatically (after years of, or continuous dys-synchronous input) as what we now consider to be behavioral disorders?

Or put another way, it doesn't matter what kinds of images our brains were processing as they evolved; they evolved with the ability to process many kinds of images, and this in turn may affect how synapses are laid down, particularly in infancy, with whatever behavioral sequelae.  There may be environments in which the brain and behavioral sequelae of this cause and effect would be beneficial, but not our current educational one.

If by chance any reader of this post knows the answers to the points of types of synchrony, refresh patterns and refresh rates, we'd greatly appreciate hearing from you--with references to the literature, and any reactions about the plausibility of the idea as a subtle cause of many different behavioral problems.

And this leads to a larger point
Whether or not Ken is on to something, the larger point is to keep in mind that environmental exposures may have unintended, unconsidered, unmeasured or even unmeasureable consequences.  Surely there are causal factors, exposures, that haven't yet even been recognized.  Epidemiology is behind a rack of eight-balls here -- we don't know if many diseases and disorders are actually increasing, we don't really know the physiology of many of them, and we don't know which environmental exposures even to suspect, which factors might have unintended effects, nor, often, how to assess them.

Indeed, correlation doesn't imply causation... except when it does.  Helicobacter pylori and stomach ulcer?  But first, someone has to think of the correlation.

Treating autoimmune disease the low-tech way?

Helminths and asthma
A map of asthma prevalence around the world shows that it is higher in industrialized parts of the world; higher in urban than rural areas, including urban Africa and South America, higher in what was West Germany after the wall came down than what was East Germany, higher in temperate zones than the tropics, and so on.  The question of why has been the subject of much research, much of that focusing on the lowly helminth, at least in tropical regions, parasitic worms that infect the gut of a high fraction of rural children in poverty.  The generic explanation for this has been the 'hygiene hypothesis,' which we wrote about here; basically, too much cleanliness can be a very bad thing.

Worldwide prevalence of asthma; from 'Global Burden of Asthma,' 2004

So, what's the mechanism that could explain the benefit of chronic helminthic infection?  The idea is that the parasites may suppress allergic inflammation, thus protecting against asthma, and indeed the allergy often associated with asthma.

A 2002 paper, e.g., in The Journal of Translational Immunology suggests:

There is good evidence that the expression of inflammation caused by helminth infections can be modulated by the host immune response, and that the failure of the expression of similar mechanisms among individuals predisposed to allergy may be responsible for the clinical expression of allergic disease. Further, there is accumulating evidence that helminth infections, particularly those caused by intestinal helminth parasites (or geohelminths) may be capable of modulating the expression of allergic disease.

Helminths and autoimmune disease in general
It turns out that a map of prevalence of any autoimmune disease around the world would show much the same trend as that of asthma -- higher prevalence in richer countries than lower, and presumably this is a true effect, not simply due to ascertainment bias based on poor access to health care in poorer parts of the world. Thus, the same question has been asked of other autoimmune 'diseases of westernization,' -- inflammatory bowel disease of Crohn's, rheumatoid arthritis, type 1 diabetes and multiple sclerosis. There are even suggestions that perhaps a third of the cases of autism could be due to autoimmune disease, as described in this piece in The New York Times in August. Could helminth infection be protective?  Many studies looking at preventing or treating these diseases with infection in mouse models have been reported, a few done in humans, including some self-experimentation, and many have been found to prevent disease entirely, or to alleviate symptoms (here's a pretty extensive table of the studies that have been done, in Parasitology Research Monographs). 

Now a piece in Nature ("Autoimmunity: the worm returns") reports the work of a gastroenterologist as he endeavors to determine the effects of helminth infection specifically on people with inflammatory bowel disease and multiple sclerosis. The author, Joel Weinstock, has worked for decades on inflammatory bowel disease, long wondering why it has become so prevalent in the last century.  He also knows his parasites, so that thinking about the possible connection between eliminating parasite infections and disease was not at all far-fetched.

Of course, as Weinstock also points out, parasite infections can have disastrous consequences, damaging the liver, bladder, or eyesight, e.g., so he had to proceed with caution.  But, as he describes, history and the map of the US seem to lend support to the idea of too much hygiene being a dangerous thing, so this was an insight he couldn't not pursue. 

In the United States and Europe, Crohn's disease first emerged in affluent populations living in hygienic conditions in the more northerly latitudes, where colder temperatures are less hospitable to soil-borne helminths. One of the last US groups to present with Crohn's disease was African Americans, who are, on average, poorer than their white counterparts. Similarly, in Europe, autoimmune diseases are more common in the richer Western Europe than in Eastern Europe.

Today, Native American reservations, which have relatively high rates of infection with parasitic worms, also have lower rates of inflammatory bowel disease. Latinos born and raised in South America rarely develop this gut disorder. If their children are born in the United States, where conditions are often more sanitary, they have a much higher risk of the disease.

Correlation does not equal causation, however, and the link had to be demonstrated. So, he began giving helminths to the mice in his lab that were models for inflammatory bowel disease, and did in fact show that they were protected against disease. He then moved on to treating volunteers, in whom he saw no adverse effects, and usually actual attenuation of disease, both bowel disease and MS. Pharmaceutical companies are now becoming interested, and double-blind studies of the effect of helminth infection on autoimmune disease are now being done.

How might parasites be protective?
Weinstock suggests that worms 'seem to have three major effects on the immune system.'  First, they cause changes in regulatory T cells so that they tone down the immune response, including autoimmune responses.  Second, they 'seem to act on other cells -- dendritic cells and macrophages,' which prevents the ramping up of the inflammatory response.  Yes, this is redundant, as Weinstock has shown in experimental studies.  And, third, they 'seem to alter the bacterial composition of intestinal flora,' in a way similar to ingesting 'probiotics,' helping to maintain intestinal health.

So if this work is right, and if cleanliness is next to godliness, it's starting to look as though the gods don't mind having a whole lot of sick people at their sides.  Weinstock is not suggesting that the industrialized world return to the heavy parasite loads of the recent past, rather that controlled infection might be a good thing.

So low-tech, and yet with the potential to eliminate a huge disease burden.  And not a word about genes!  Of course, one can expect the massive, heavy-handed vested gene industry to start to argue about genetic variation in susceptibility to the parasites.  Of course there will be some of that, but it is likely to be more GWAS minutiae rather than major causal factors.

BUT!
Herein we must add a caution, however.  One-size-fits-all explanations are rife these days, and it seems unlikely that intestinal worms could explain so many increasing disorders of different types. Usually, the miracle discovery turns out to be a mirage, relevant in some particulars but usually minor ones.  In this case, neither the immune system nor autoimmune diseases nor how the immune system responds to infection with helminths is well-enough understood for the cause and effect here to be convincing.

Empirical data seem suggestive, but the tropics/temperate zone gradient is also associated with numerous other factors, which has lead, e.g., to the sun/vitamin D exposure hypothesis with respect to multiple sclerosis, and clustering of cases has been suggestive of infectious causation.  The hygiene hypothesis is not confirmed in all studies, and data quality is surely not comparable across regions of the world, and so on.  A lot of caveats.  We'll just have to see how this one turns out.

Chronic diseases due to infection?

A new paper in the journal PLOS Pathogens, described here, reports that hypertension, or high blood pressure, may be due to infection with cytomegalovirus (CMV) in humans. At least it is in mice, particularly when combined with a high-cholesterol diet. Other viral infections have been associated with hypertension, but the focus in recent years has been on finding genes for high blood pressure.

Most people are infected with CMV--60 to 99%, according to the PLOS paper--and not everyone with CMV has hypertension, but if this new study is correct, it seems to be a significant risk factor. But, as we've discussed repeatedly here, recent attention has been on identifying genetic causation for common diseases, of which hypertension is one. Indeed, schools of public health had all but eliminated infectious disease programs in the '70s and '80s, because we'd conquered infectious diseases (it was rather confidently thought!) and the challenge was now to conquer chronic disease, which first seemed to be an environmental/lifestyle challenge. After that didn't work very well, attention turned to genes (real science--molecules!) and it seemed hopeful that we were going to explain the common complex diseases in genetic terms.

Why the infatuation with genes? Genes follow rules of inheritance, and human genetics was successfully finding genes for rare usually pediatric diseases like PKU or Tay Sachs, and most traits including disease concentrate at least somewhat in families, so it seemed that the same approach should be applicable to chronic disease.

It was clear (though less of interest to geneticists) that even with aggregation in families, environmental factors such as modern inactive over-fed lifestyles etc. could explain a lot. But with some exceptions, the extension of epidemiology to genetics was from concern with environmental exposures, not infection--the 'old' kind of cause that had been beaten by hygiene and vaccinations. But, it may be that a lot of chronic disease is infectious after all--a real lesson in humility if it turns out to be true!

So it turns out that infectious disease biology is a mix of real and important genetics (host, virus, molecular therapy and prevention), as well as the complexities of environmental exposures, plus complexity generally, etc. If genetics resources are concentrated on these kinds of molecular interactions, rather than trying to explain disease risk by host variation (which GWAS and other studies of these diseases clearly show will not be the major cause), then we have some interesting scientific days ahead. This could be--should be--an area where genetic approaches are entirely appropriate rather than forced. The relationships between host and parasite represent battles and evolution at the molecular level--mano a mano among molecules, so it is appropriate to study it at that level.

Paul Ewald has been saying for some time that infectious causes are more important, even for chronic late-onset diseases, than had been thought. He was largely ignored. Maybe he's right!