Tolstoy rides again — Schizophrenia

“A field plagued by inconsistency, and perhaps even a degree of charlatanism” — strong stuff indeed [ Neuron vol. 83 pp. 760 – 763 ’14 ]. They are talking about studies attempting to find the genetic causes of schizophrenia.

This was the state of play four and a half years ago (in a post of April 2010)

Happy families are all alike.; every unhappy family is unhappy in its own way”. Thus beginneth Anna Karenina. That wasn’t supposed to happen with hereditary disease. The examples we had before large scale DNA sequencing became cheap were basically one gene causing one disease. Two of the best known cases were sickle cell anemia and cystic fibrosis. In the former, a change in a single position (nucleotide) of DNA caused a switch of one amino acid (valine for glutamic) acid at position #6 in beta hemoglobin. In the latter, all mutations have been found in a single gene called CFTR. 85% of known mutations involve the loss of 1 amino acids. But by 2003 over 600 different mutations accounted for only part of the other 15%. There is plenty of room for mutation as CFTR has 1480 amino acids. The kids I took care of in the muscular dystrophy clinic all turned out to have mutations in the genes for proteins found in muscle.

Why not look for the gene causing schizophrenia? It’s a terrible disease (the post “What is Schizophrenia really Like?” is included after the **** ) with a strong hereditary component. There was an awful era in psychiatry when it was thought to be environmental (e.g. the family was blamed). Deciding what is hereditary and what is environmental can be tricky. TB was thought to be hereditary (for a time) because it also ran in families. So why couldn’t schizophrenia be environmental? Well, if you are an identical twin and the other twin has it, your chances of having schizophrenia are 45%. If you are a fraternal twin your chance of having it are 3 times less (15%). This couldn’t be due to the environment.

It’s time to speak of SNPs (single nucleotide polymorphisms). Our genome has 3.2 gigaBases of DNA. With sequencing being what it is, each position has a standard nucleotide at each position (one of A, T, G, or C). If 5% of the population have one of the other 3 at this position you have a SNP. By 2004 some 7 MILLION SNPs had been found and mapped to the human genome. So to find ‘the gene’ for schizophrenia, just take schizophrenics as a group (there are lots of them — about 1% of the population) look at their SNPs, and see if they have any SNPs in common.

Study after study found suspect SNPs (which can be localized exactly in the genome) for schizophrenia. The area of the genome containing the SNP was then searched for protein coding genes to find the cause of the disease. Unfortunately each study implicated a different bunch of SNPs (in different areas of the genome). A study of 750 schizophrenics and an equal number of controls from North Carolina used 500,000 SNPs. None of the previous candidate genes held up. Not a single one [ Nature vol. 454 pp., 154 – 157 ’08 ]

As of 2009 here are 3,000 diseases showing simple inheritance in which a causative gene hasn’t been found. This is the ‘dark matter’ of the genome. We are sure it exists (because the diseases are hereditary) but we simply can’t see it.

There is presently a large (and expensive) industry called GWAS (Genome Wide Association Studies) which uses SNPs to look for genetic causes of diseases with a known hereditary component. One study on coronary heart disease had 23,000 participants. In 2007 the Wellcome Trust committed 45 million (pounds? dollars?) for studies of 27 diseases in 120,000 people. This is big time science. GWAS studies have found areas of the genome associated with various disorders. However, in all GWAS studies so far, what they’ve picked up explains less than 5% of the heritability. An example is height (not a disease). Its heritability is 80% yet the top 20 candidate genetic variants identified explain only 3% of the variance. People have called for larger and larger samples to improve matters.

What’s going on?

It’s time for you to read “Genetic Heterogeneity in Human Disease” [ Cell vol. 141 pp. 210 – 217 ’10 ]. It may destroy GWAS. Basically, they argue that most SNPs are irrelevant, don’t produce any functional change, and have arisen by random mutation. They are evolutionary chaff if you will. A 12 year followup study of 19,000 women looked at the 101 SNPs found by GWAS as risk variants for cardiovascular disease — not one of them predicted outcome [ J. Am. Med. Assoc. vol. 303 pp. 631 – 637 ’10 ]. The SNPs haven’t been eliminated by natural selection, because they aren’t causing trouble and because the human population has grown exponentially.

There’s a lot more in this article, which is worth reading carefully. It looks like what we’re calling a given disease with a known hereditary component (schizophrenia for example) is the result of a large number of different (and rather rare) mutations. A given SNP study may pick up one or two rare mutations, but they won’t be found in the next. It certainly has been disheartening to follow this literature over the years, in the hopes that the cause of disease X, Y or Z would finally be found, and that we would have a logical point of attack (but see an old post titled “Some Humility is in Order”).

Is there an analogy?

200 years ago (before Pasteur) physicians classified a variety of diseases called fevers. They knew they were somewhat different from each other (quotidian fever, puerperal fever, etc. etc.). But fever was the common denominator and clinically they looked pretty much the same (just as dogs look pretty much the same). Now we know that infectious fever has hundreds of different causes. The Cell article argues that, given what GWAS has turned up so far, this is likely to be the case for many hereditary disorders.

Tolstoy was right.

Fast forward to the present [ Nature vol. 511 pp. 412 – 413, 421 – 427 ’14 ] This is a paper from the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) which analyzed some 36,989 and 113,075 controls. They found 128 independent associations spanning 108 conservatively defined loci, meeting genome-wide significance. 83/128 hadn’t been previously reported. The associations were enriched among genes expressed in brain. Prior to this work, some 30 schizophrenia associated loci had been found through genome wide association studies (GWAS).

Interestingly 3/4 of the 108 loci include protein coding genes (of which 40% represent a single gene and another 8% are within 20 kiloBases of a gene).

The editorial noted that there have been 800 associations ‘of dubious value’

The present risk variants are common, and contribute in most (if not all) cases. One such association is with the D2 dopamine receptor, but not with COMT (which metabolizes dopamine). The most significant association is within the major histocompatibility complex (MHC).

The paper in Neuron cited above notes that schizophrenia genetics is a “field plagued by inconsistency, and perhaps even a degree of charlatanism” and that that there have been 800 associations ‘of dubious value’. The statistical sins of earlier work are described resulting in many HUNDREDS of variant associations with schizophrenia, and scores of falsely implicated genes. Standards have been developed to eliminate them [ Nat. Rev. Genet. vol. 9 pp. 356 – 369 ’08 ].

Here is their description of the statistical sins prior to GWAS “Before GWAS, the standard practice for investigating schizophrenia genetics (as well as many other areas) was to pick a candidate gene (usually based on dopamine or glutamate pathways or linkage studies) and compare the frequency of genetic variants in cases and controls. Any difference with a p value 0.05) p values, and associations seen in partitions of a data set. Beyond all of these obvious statistical transgressions, these studies often entirely ignored well-established causes of spurious associations such as population stratification. Labs would churn out separate papers for gene after gene with no correction for multiple testing, and, on top of all of that, there was a publication bias against negative findings. “

There is a hockey stick model in which few real associations aren’t found until a particular sample size is breached. This works for hypertension.

GWAS identifies genomic regions, not precise risk factors. It is estimated the the 108 loci implicate 350 genes. However the Major Histocompatibility Complex (MHC) counts as one locus and it has tones of genes.

The NHGRI website tracks independent GWAS signals for common diseases and traits, and currently records 7,300 associations with a p value under 5 * 10^-8. Only 20 have been tracked to causal variants (depending on criteria.

The number of genes implicated will only grow as the PGC continues to increase the sample size to capture smaller and smaller effect sizes — how long will it be until the whole genome is involved? There are some significant philosophical issues involved, but this post is long enough already.

What Schizophrenia is really Like

I feel that writing to you there I am writing to the source of a ray of light from within a pit of semi-darkness. It is a strange place where you live, where administration is heaped upon administration, and all tremble with fear or abhorrence (in spite of pious phrases) at symptoms of actual non-local thinking. Up the river, slightly better, but still very strange in a certain area with which we are both familiar. And yet, to see this strangeness, the viewer must be strange.”

“I observed the local Romans show a considerable interest in getting into telephone booths and talking on the telephone and one of their favorite words was pronto. So it’s like ping-pong, pinging back again the bell pinged to me.”

Could you paraphrase this? Neither can I, and when, as a neurologist I had occasion to see schizophrenics, the only way to capture their speech was to transcribe it verbatim. It can’t be paraphrased, because it makes no sense, even though it’s reasonably gramatical.

What is a neurologist doing seeing schizophrenics? That’s for shrinks isn’t it? Sometimes in the early stages, the symptoms suggest something neurological. Epilepsy for example. One lady with funny spells was sent to me with her husband. Family history is important in just about all neurological disorders, particularly epilepsy. I asked if anyone in her family had epilepsy. She thought her nephew might have it. Her husband looked puzzled and asked her why. She said she thought so because they had the same birthday.

It’s time for a little history. The board which certifies neurologists, is called the American Board of Psychiatry and Neurology. This is not an accident as the two fields are joined at the hip. Freud himself started out as a neurologist, wrote papers on cerebral palsy, and studied with a great neurologist of the time, Charcot at la Salpetriere in Paris. 6 months of my 3 year residency were spent in Psychiatry, just as psychiatrists spend time learning neurology (and are tested on it when they take their Boards).

Once a month, a psychiatrist friend and I would go to lunch, discussing cases that were neither psychiatric nor neurologic but a mixture of both. We never lacked for new material.

Mental illness is scary as hell. Society deals with it the same way that kids deal with their fears, by romanticizing it, making it somehow more human and less horrible in the process. My kids were always talking about good monsters and bad monsters when they were little. Look at Sesame street. There are some fairly horrible looking characters on it which turn out actually to be pretty nice. Adults have books like “One flew over the Cuckoo’s nest” etc. etc.

The first quote above is from a letter John Nash wrote to Norbert Weiner in 1959. All this, and much much more, can be found in “A Beatiful Mind” by Sylvia Nasar. It is absolutely the best description of schizophrenia I’ve ever come across. No, I haven’t seen the movie, but there’s no way it can be more accurate than the book.

Unfortunately, the book is about a mathematician, which immediately turns off 95% of the populace. But that is exactly its strength. Nash became ill much later than most schizophrenics — around 30 when he had already done great work. So people saved what he wrote, and could describe what went on decades later. Even better, the mathematicians had no theoretical axe to grind (Freudian or otherwise). So there’s no ego, id, superego or penis envy in the book, just page after page of description from well over 100 people interviewed for the book, who just talked about what they saw. The description of Nash at his sickest covers 120 pages or so in the middle of the book. It’s extremely depressing reading, but you’ll never find a better description of what schizophrenia is actually like — e.g. (p. 242) She recalled that “he kept shifting from station to station. We thought he was just being pesky. But he thought that they were broadcasting messages to him. The things he did were mad, but we didn’t really know it.”

Because of his previous mathematical achievments, people saved what he wrote — the second quote above being from a letter written in 1971 and kept by the recipient for decades, the first quote from a letter written in 12 years before that.

There are a few heartening aspects of the book. His wife Alicia is a true saint, and stood by him and tried to help as best she could. The mathematicians also come off very well, in their attempts to shelter him and to get him treatment (they even took up a collection for this at one point).

I was also very pleased to see rather sympathetic portraits of the docs who took care of him. No 20/20 hindsight is to be found. They are described as doing the best for him that they could given the limited knowledge (and therapies) of the time. This is the way medicine has been and always will be practiced — we never really know enough about the diseases we’re treating, and the therapies are almost never optimal. We just try to do our best with what we know and what we have.

I actually ran into Nash shortly after the book came out. The Princeton University Store had a fabulous collection of math books back then — several hundred at least, most of them over $50, so it was a great place to browse, which I did whenever I was in the area. Afterwards, I stopped in a coffee shop in Nassau Square and there he was, carrying a large disheveled bunch of papers with what appeared to be scribbling on them. I couldn’t bring myself to speak to him. He had the eyes of a hunted animal.

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