Barcelona vista

Here are some thoughts about the second stop on our trip, Barcelona. We visited with Jaume Bertranpetit, and I gave a lecture to his group at the University of Pampeu Fabra. The group includes many other fine scientists in a beautiful new building, and we met a few who we had not known before. We had both previously been there as part of the PhD committee of one of Jaume's and his colleague Francesc Calafell's students. It was great to see these long-standing friends.

We were there during the weeklong festival of La Mercè, honoring one of the patron saints of the city, so it's only fitting to include a photo of some of the street theatre we saw. These guys were on flexible poles, swaying to music, an act we'd never seen the likes of before.

Jaume's group is probably the leading light of Spain in genetics, if not all of southern Europe, and has been very productive in front-line human genetics for some time. Our familiarity is with their work on human variation and history, especially of Europe (e.g., the reasons for the distribution of alleles associated with cystic fibrosis). They also do biomedical genetics, and are quite aware of the problems with GWAS and related ideas of how to map and deal with the genetic component of human disease. I talked on that subject, explaining how evolutionary perspectives show why we are finding what we are finding: that many important and common diseases that seem to have substantial heritability are not yielding to large-scale association mapping efforts. One member, Sergi Valverde, is a computational biologist trying to wrestle with the network approach to the problem.

We learned of a paper by Jaume and others in BMC Genomics, that looks at the degree of genetic difference in the 'isolate' populations compared to other 'main' populations. There it's reported that while the isolates do have some genetic differences from the surrounding populations, they are not really as different as their language.

A classic example, though not one reported in this paper but in another unpublished paper by Dr Bertranpetit is the Basques. Their isolation was long argued based on their language (apparently ancient and unrelated to what is spoken by surrounding populations in Spain and France).

This is relevant beyond the long-standing interest in the history of the Basques, about which many classical papers in human genetics from the early to mid 20th century were written, or other individual isolates. After a few traits were found to be at high frequency in religious isolates (like the US Amish and Hutterites, in Finland and in French Canadians), the idea became widespread that the bottleneck in population size would simplify the genetic causal basis of otherwise more complex diseases, making them more mappable in the isolate.

This general isolate or bottleneck effect goes beyond the classical reason, that founder effects (recent founding by a small number of people, plus increased relative degree of inbreeding in the small descendant population) would by chance raise the frequency of some recessive traits to high enough frequency that they could be mapped and understood.

However, in the Basques as elsewhere (as Bertranpetit and colleagues note), the degree of bottleneck and the amount of subsequent gene flow with surrounding populations, the differences are far less than was expected. They are not proving to be the mapping bonanza that was hoped. Given the amount of variation that exists, the amount of population constriction needed to do what was hoped is far more severe than has been the case. Of course, isolates may have some disorders at higher frequency, or some very rare causal genotypes basically not found frequently enough elsewhere to be useful. But overall, isolates were over-sold.

In fact, the relative success in mapping in the Iceland population, which was touted in advance as having great potential largely to their isolate status, has proven to be due mainly to the availability of large genealogies, not isolate status. Even in Finland, where much of the hope for the usefulness of isolates was fueled about 15 years ago, and where the local population history can be reconstructed, there has not been a high yield of new genotypes found to be associated with common disease.

The Basque story shows that one has to be circumspect about ideas that seem plausible but can be accepted too uncritically. The history of the Basques is more interesting as history than as genetics. That's a subject too great to go into but a fine book, The Basque History of the World by Mark Kurlansky provides some good material, as does web searching, etc.

Besides seeing our daughter newly installed as a post-graduate violin student in Barcelona, we had a very fine time visiting with Jaume and others. It is a city worth visiting, and Jaume's is a scientifically vibrant group. In addition to his science, he is an effective politician when it comes to program building and resource acquisition. For the last few years he has been the very successful director of a program to recruit scientists to Catalunya, ICREA, which may still be looking for new people, in case any reader might be interested.

-Ken

The simple facts of life

Here we report on some reflections after our participation in a meeting on the 'New Genomics in Medicine and Public Health' held at the University of Bristol, UK. The talks were varied and interesting, including a talk about Mendel, reports of successful and unsuccessful genomewide association studies, plaudits for the UK Biobank, and discussion of clinical applications of genomic findings.

An important question these days, related to various methods in genetics and its role in medicine and public health, is how causally complex life really is--a question at the heart of most of the work reported at the meeting. Some normal traits as well as diseases clearly are genetic, in that their variation is clearly caused by variation in a single gene (or a small number of genes, in a way that's well understood). But others are less clear cut, as we've discussed here a number of times.

Vested interests of all sorts, including venal and careerist interests, but also strongly held scientific conviction affect this area these days. One way to put the question is: "How causally complex is life?" Here the interest is mainly in genes, environment getting some but usually rather casual or minimal attention, and the question boils down to how well phenotypes can be predicted from known or knowable genotypes. Sometimes this means using individual variation to predict individual disease risk--this is the major original purpose of GWAS (genome-wide association studies). Sometimes it means using natural variation as a tactic to identify genetic pathways that are responsible for some normal trait; the idea here is either that, when mutant, the pathway (or 'systems' or 'network') genes could lead to disease and/or that these genes, when known, can be used as general preventive or therapeutic targets.

A commonly invoked motivation for human genetics work these days is that we will be able to implement 'personalized medicine', to predict disease or treatment, or to suggest preventive measures, based on each person's genotype. Many companies are promoting this, and the molecular genetics community is hyping it very heavily (here, there is no doubt of strong material vested interests, even if some actually believe it will work as advertized).

There are hundreds of diseases for which a, or often the causative gene is known. Sickle cell anemia, Huntington disease, Phenylketonuria (PKU), Cystic fibrosis (CF), and Muscular dystrophy (MD) are just a few examples. For these, predictive power already exists, though clinical application is not necessarily based on genotype. There are other examples where the latter is true, but these are generally rare in the population. Promising gene-based molecular therapy is in the works for CF, MD, and maybe even for some forms of inherited breast cancer (due to BRCA1/2 mutations). For these diseases, causation is clear even if there are substantial variation in risk, age of onset, or severity. Causation here is usually thought of as simple.

But for most common and/or chronic diseases, the story is far from clear as we've mentioned in various earlier posts (and as is widely discussed in the literature). These traits usually have substantial heritability (i.e., familial risk--if a close family member is affected, your chance of getting the same disease is greater than that of a random member of the population to which you belong). That means that, unless we are somehow badly understanding things, genetic variation plays a major role in risk (at least in current environments). Yet after many sophisticated, large studies, identified genes account for only a small fraction of the familial risk. The data suggest that many genes, say 'countless' genes, contribute substantial risk in aggregate, but individually their contribution is so small as to be unidentifiable by feasible (or cost-justifiable) studies. That would suggest that the disorder is caused by numerous combinations of huge numbers of individually weak, and rare, genetic variants. This is known classically as 'polygenic' inheritance, and if it's what's going on, things are very complex indeed.

Others, focused on the many clearly 'Mendelian' (single-gene) traits, simply don't believe life is that complicated. They suggest at least two other possibilities. One is that only a modest number of genes contribute, but most of the culpable alleles (sequence variants) are so rare and weak that genomewide association studies cannot pick them up. At such genes, there may be one or two strong, common alleles and these have high penetrance (when present, the disease usually occurs) and so they can be identified in family or GWAS. Those variants only account for a small amount of overall genetic contributions. But once the gene is known, we can sequence it in many patients and, lo and behold!, we find many other alleles that, some argue, contribute the rest of the observed family risk.

There is some truth to this: we have done simulations to show that there can be high heritability but only a few contributing genes, for just such reason (heterogeneity of the frequency and effects of existing alleles).

Another possibility is that a modest number of genes have variants with rare, but not very rare frequency. These will be identified by the panoply of existing methods, and once that's done it will be possible to genotype everyone at these genes, identify each person's individual set of variants, and determine risk. These are called 'oligogenic' effects, because the number of genes involved is small rather than huge. This view acknowledges the current problem, but assumes it will go away with enough data--and, importantly, that business as usual is a right approach.

Presentations at the Bristol meeting, including Ken's, show clearly that causation is a spectrum of aggregate vary rare genetic effects, a larger but still small fraction of oligogenic effects, major gene effects, and polygenic effects.

The question is: what do we do if this is true? Where is the practical limit below which attempts to identify all the genes are futile or not worth the investment, and is it likely that current attempts will at least identify the bulk of genetic effects and the networks involved so that the disease can be eliminated in whole or at least major part?

There is no single consensus in this area. Some are more skeptical than others. Some argue that knowing the genetic contributions to disease may make diagnosis more specific (the doctor can test for which gene is contributing to a given case), even if genotype-based prediction will remain a dream in the eyes of venture capitalists. Other computophiles believe that if enough computers are used on enough DNA sequence, the problem will, like infectious diseases, be solved. We won't be sick any more.

Time will tell where in the causal spectrum most traits lie. One thing we can be sure of, though: in this contentious area in which huge career, institutional, and commercial investments are at stake, in years to come, retrospective evaluation will always claim victory! Few will look back and say that we knew better than to make the level of investment in genetic causation that we are currently making.

The rear-view mirror and the road ahead

We've already posted some critiques of the current push for ever-larger genomewide association-style studies of disease (GWAS) which have been promoted by glowing promises that huge-scale studies and technology will revolutionize medicine and cure all the known ills of humankind (a slight exaggeration on our part, but not that far off the spin!). We want to explain our reasoning a bit more.

For many understandable reasons, geneticists would love to lock up huge amounts of research grant resources, for huge amounts of time, to generate huge amounts of data that will be deliciously interesting to play with. But such vast up-front cost commitments may not be the best way to eliminate the ills of humankind. It may not even be the best way to understand the genetic involvement in those ills.

In a recent post we cited a number of our own papers in which we've been pointing out problems in this area for many years, and while we didn't give references we did note that a few others have recently been saying something like this, too. The problem is that searching for genetic differences that may cause disease is based on designs such as comparing cases and controls, which don't work very well for common, complex diseases like diabetes or cancers. Among other reasons this is because, if the genetic variant is common, people without the disease, the controls, may still carry a variant that contributes to risk, but they might remain disease-free because, say, they haven't been exposed to whatever provocative environment is also associated with risk (diet, lack of exercise, etc.). And these designs don't work very well for explaining normal variation.

As we have said, the knowledge of why we find as little as we are finding has been around for nearly a century, and it connects us to what we know about evolutionary genetics. Since the facts apply as well to almost any species--even plants, inbred laboratory mice, and single-celled species like yeast--they must be telling us something about life that we need to listen to!

Part of the problem is that environments interact with many different genes to produce the phenotypes (traits, including disease) in ways that would be good to understand. However, our methods of understanding causation necessarily look backwards in time (they are 'retrospective'): we study people who have some trait, like diabetes, and compare them to age-sex-etc. matched controls, to see how they differ. Geneticists and environmental epidemiologists stress their particular kinds of risks, but the trend recently has strongly been to focus on genes, partly because environmental risk factors have proven to be devilishly hard to figure out, and genetics has more glamour (and plush funding) these days: it may have the sexy appearance of real science, since it's molecular!

Like looking in the rear-view mirror, we see the road of risk-factor exposures that we have already traveled. But what we really want to understand is the causal process itself, and for 'personalized medicine' and even public health we need to look forward in time, to current people's futures. That is what we are promising to predict, so we can avoid all ills (and produce perfect children).

We need to look at the road ahead, and what we see in the rear-view mirror may not be all that helpful. We know that the environmental component of most common diseases contributes far more to risk than any specific genetic factors, probably far more than all genetic factors combined do on their own. We know that clearly from the fact that many if not most common diseases have changed, often dramatically, in prevalence just in the last couple of generations, while we've had very good data and an army of investigators tracking exposures, lifestyles, and outcomes.

Those changes in prevalence are a warning shot across the genetics bow that geneticists have had a very convenient tin ear to. They rationalize these clear facts by asserting that changes in common diseases are due to interactions between susceptible genotypes and these environmental changes. Even if such unsupported assertions were true, what we see in the rear-view mirror does not tell us what the road ahead will be like, for the very simple, but important reason that there is absolutely no way to know what the environmental--the non-genetic--risk factor exposures will be.

No amount of Biobanking will change this, or make genotype-based risk prediction accurate (except for the small subset of diseases that really are genetic), because each future is a new road and risks are inevitably assessed retrospectively. Even if causation were relatively simple and clear, which is manifestly not the case. No matter how accurately we can identify the genotypes of everyone involved (and there are some problems there, too that we will have to discuss another time).

This is a deep problem in the nature of knowledge in regard to problems such as this. It is one sober, not far-out, not anti-scientific, reason why scientists and public funders should be very circumspect before committing major amounts of funding, for decades into the future, to try to track everyone, and everyone's DNA sequences. And here we don't consider the great potential for intrusiveness that such data will enable.

As geneticists, we would be highly interested in poking around in the data mega-studies would yield. But we think it would not be societally responsible data to generate, given the other needs and priorities (some of which actually are genetic), that we know we can address with available resources and on other problems or approaches.

We can learn things by checking the rear-view mirror, but life depends on keeping our eye on the road ahead.