Schizophrenia and bipolar disorder genetics blog

Posting has been a little ’sparse’ in the last few months but here are a couple of links to comments I’ve made on the SRF site on two recent interesting papers:

Firstly, a look at how searching for identicial (homozygous) regions in the human genome may be a way to track down recessive mutations causing psychiatric illness.

Secondly, a description of a new MR imaging approach which can distinguish the newly forming brain cells in the hippocampus……these cells might be important in the aetiology of psychiatric illness.

I’ll try to write a ‘year in summary’ piece in the next few weeks……

Perhaps it was the frustration over the slow speed of progress in the identification of complex disease genes, or maybe the fact that we live in an era where Big Science has become routine, or even the rapid improvements and cost reductions in the facilitating genetic technologies. Whatever it was, someone woke up one morning and said “How about solving seven genetic disorders at once�. The results of this seeming pipe dream reached fruition recently in the form of a titanic Nature paper and its gargantuan accompanying supplementary online data. Of relevance to this blog is the fact that bipolar disorder is among the list of diseases which also number coronary artery disease, hypertension, Crohn’s disease, rheumatoid arthritis, type 1 and type 2 diabetes.

Genome-wide association (GWA) is the name of the procedure which has been used here – not a new technique, as such, but a new scale with which to apply the familiar case-control association study approach that I have mentioned previously. Instead of the hundreds of cases and handful of markers tested in previous case-control association studies, GWA experiments use thousands of samples (for the bipolar study it was 2000 cases and 3000 controls) and over half a million markers, the idea being that it is both an objective and also statistically rigorous screen. The grand scale of this approach cuts both ways: the greater sensitivity and coverage is accompanied by the greater risk of false positives. Such large numbers mean that, by chance alone, particular markers will appear to be associated but in reality are not. To this end, the statisticians have been busy trying to figure out the thresholds which have to be achieved to separate the real from the spurious. Here, the statistics are pretty much reduced to rank ordering of the p-values associated with each marker and some comparison between disease.

The Wellcome Trust coughed up £8 million to fund this study but it is not the first to reach publication.

The Malhotra group published a schizophrenia WGA earlier this year (admittedly with a rather small sample size) which identified one candidate gene, CSF2RA (colony stimulating factor, receptor 2 alpha) on the X/Y pseudoautosomal region.

More relevant to the Nature paper is last month’s paper from the McMahon et al. group detailing the results of their bipolar disorder GWA.

In that paper 461 cases and 563 controls were tested from the US population and 772 cases and 876 controls from the German population but using a pooling protocol rather than an individual genotyping approach. Positive findings from this first stage (1887/550,000 SNP markers) had fulfilled criteria such as being reasonably common frequency, of reasonable strength of effect and located near a gene. These are somewhat arbitrary, especially the last one (regulatory mutations have been found up to 2000000 base pairs away from genes), but are a necessary start in terms of cost feasibility at the small-lab scale. The positive findings were replicated through individual genotyping of a large set of German samples and the surviving SNP markers identified.

Before we look at the results I have to register my concerns over the use of entirely family-derived samples in the US population group. Not only do I believe such samples are inappropriate for use in a protocol designed to find low penetrance general risk factors (see a previous post), but I also think that the fact that the German sample was only 13% family-derived meant that it was not an ideal comparative study group.

However, having said this, it is up to me to explain how the experiment came up with positive findings. 88/1887 US positive SNPs were replicated between the two geographic populations and a proportion of a subset of these also survived being genotyped individually too. My current thoughts are that perhaps the success of these studies derives just as much from the power of the controls (not subject to familial influences but necessarily reduced in population risk alleles) .

80 genes were identified and, of these, Diacylglycerol Kinase Eta (DGKH) and SORCS2 seem to contain the most positive SNPs each and reasonable odds ratio values (a measure of their strength of effect). The former of these genes can be connected to the lithium-sensitive phosphatidyl inositol pathway (thus providing a potential link to a commonly used treatment regime) whereas the latter is much less well characterised.

So how does this compare with the mother of all GWAs from Wellcome? Well, there is no clear evidence of large-scale overlap between the McMahon and Wellcome results although, to be fair, the papers were published so close together that no comparisons were actually formally carried out. Even though DGKH and SORCS2 are absent from the top-ranking gene list there are some very interesting points of overlap (see below). But before that, some bad news: of the seven diseases tested, bipolar disorder was, on the face of it, among the least productive. For many of the others, previously suspected genes were nicely confirmed and those fields now also have a set of novel genes to analyse – some intriguingly spanning disease boundaries. Bipolar disease failed to have any such big-hitting genes identified. All we are left with are some moderately associated genes. Before I go into the properties of those genes, we must address the possible reasons for the lack of dramatic success. Again, I am not sure of the sample selection criteria for the study in terms of familial versus sporadic cases but, perhaps more importantly, the other diseases studied all have bona fide quantifiable diagnostic criteria –we just don’t have this in psychiatry where it’s not possible to take a reading like blood pressure, lung capacity, blood sugar levels etc. So I think there’s clearly an issue of non-homogeneity of the bipolar classification. This is a very hard problem to solve: the irony is that perhaps the only biomarkers for psychiatric illness will be the genetic markers that we have yet to clarify. Despite this rather circular problem, I’m very much against the notion, proposed in some quarters, that psychiatric disorders have some special genetic qualities that render them immune to such genetic approaches.

I’ve had a look at the markers which show strong/moderate and moderate association with bipolar disorder in the Wellcome study. The paper did very little in the way of overspeculation on the function of the identified genes. Dynactin 5 was mentioned because it interacts with one of our lab’s key genes, DISC1. KCNC2 which encodes a potassium channel, GABRB1 and GRM7 both encoding neurotransmitter receptors, and SYN3, a synaptic protein were also briefly discussed. This was a very general paper and I guess there wasn’t the space to expand beyond these. For your amusement, I’ve annotated the top-most genes in a pretty haphazard way. For those in the field, it might prove useful to be able to quickly scan down this list for any of interest.

The list starts off with the chromosome number, then the SNP marker i.d. (rs number) and finally the rough description of genes in the region. If you type the SNP i.d. or gene name into the human genome browser dialogue box and click ‘submit’, you’ll be able to look at the genomic locale and link out to other information on the gene (especially OMIM for neat biographies of the genes). See how many of the associations are nowhere near known genes (or occasionally near ‘ests’ which are uncharacterised possible genes).

Strong or moderate associations

1 rs2989476 Nothing near
2 rs4027132 LIPIN1 and est
2 rs7570682 est
2 rs1375144 DPP10, dipeptidyl peptidase 10 isoform long
2 rs11888446 Nothing near
2 rs4673905* DNA polymerase-transactivated protein 6 (DNAPTP6) mRNA
2 rs2953145 ANKMY1, DUSP28, MPEPL1, CAPN10, GPR35
3 rs4276227 CMTM8, CKLF-like MARVEL transmembrane domain containing…chemokine like
3 rs9834970 Serine/threonine-protein kinase DCAMKL3 (EC 2.7.11.1) (Doublecortin- like and CAM kinase-like 3) and KIAA0342 protein (Fragment).
3 rs683395 LAMP3, lysosomal-associated membrane protein 3
6 rs6458307 TBCC (beta-tubulin cofactor C|), KIAA0240
6 rs6901299 TRDN, triadin1 calcium receptor interacting
7 rs1405318 KIAA0960 protein
8 rs2609653 Nothing near
9 rs10982256 DFNB31= CASK-interacting protein CIP98 isoform 1, =whirler mouse mutant
14 rs10134944 SLC35F4, solute carrier family 35 member F4,
14 rs11622475 TDRD9, tudor domain containing 9,
16 rs420259 PALB2, (partner and localizer of BRCA2) and DCTN5 (dynactin5)
16 rs1344484 quite a way from CHD9, chromodomain helicase DNA binding protein 9
20 rs3761218 CDC25B, cell division cycle 25B isoform 2,
X rs975687 CAPN6, calpain6

Moderate strength associations

1 rs10888879 PARS2 (prolyl-tRNA synthetase), ttc22 (tetratricopeptide repeat domain 22)
1 rs10889189 Nothing near
1 rs4916031 AK3L1 (adenylate kinase 3-like 1 isoform 7)
1 rs6691577 LRRC1 (leucine rich repeat containing 7)
1 rs1776905 Nothing near
1 rs10779279 ESRRG (estrogen-related receptor gamma isoform 2)
1 rs12070036 zinc finger protein 678
2 rs2049674 TMEM17 quite a way away
2 rs17029753 Nothing near
2 rs13386690 DPP10, dipeptidyl peptidase 10 isoform long
2 rs4407218 not in database
2 rs4673905* DNA polymerase-transactivated protein 6 (DNAPTP6) mRNA
3 rs1485171 GRM7, metab glut receptor
3 rs6762678 ZNF659, zinc finger 659
3 rs711715 Nothing near
3 rs4858594 THRB, thyroid hormone receptor beta
3 rs33460 CCK1(cholecystokinin preproprotein), lyzl4(lysozyme-like 4)
3 rs13074575 PTPRG1, protein tyrosine phosphatase receptor type G
4 rs7680321 GABRB1, gaba receptor
4 rs1996755 DKFZp586K0717
5 rs5009031 Nothing near
5 rs1428006 Nothing near
5 rs17701996 FBL3B/FBXL21( F-box and leucine-rich repeat protein 21…ubiquitin ligase), LECT2 (leukocyte cell-derived chemotaxin 2 precursor) cluster
5 rs999580 Nothing near
6 rs365237 NHLRC1 (malin ubiquitin ligase), tpmt1(thiopurine S-methyltransferase), AOF1 (amine oxidase (flavin containing) domain 1), DEK oncogene
6 rs6926599 ests
6 rs17739564 TRDN, triadin1 calcium receptor interacting
6 rs6906574 MOXD1, monooxygenase DBH-like 1 isoform 2, senescence protein
6 rs2763025 SYNE1, = synaptic nuclear envelope protein 1=nesprin 1 isoform longer,
7 rs2286492 FAM126A = down-regulated by Ctnnb1 = myelination gene involved in congenital cataract
8 rs2875734 Nothing near
8 rs16919670 Nothing near
8 rs9643449 Nothing near
8 rs10097578 ZNF706 quite a way away
8 rs1993980 TRAP25, TRAP/Mediator complex component TRAP25, thyroid hormone receptor-associated protein 6
9 rs7030123 Nothing near
9 rs1573257 PAX5, paired box 5
9 rs10993698 SYK, spleen tyrosine kinase
9 rs4978927 SVEP1, sushi, von Willebrand factor type A, EGF and pentraxin domain containing 1
9 rs10982246 DFNB31= CASK-interacting protein CIP98 isoform 1, =whirler mouse mutant
10 rs788261 Nothing near
10 rs10826258 Nothing near
10 rs1866437 similar type of clusters as
10 rs7896131 HHEX1 AND EXOC1 quite a way away
10 rs2096285 PTPRE, protein tyrosine phosphatase receptor type E
11 rs858719 ZBTB44, BTB (POZ) domain containing 15
12 rs7136898 SOX5, SRY (sex determining region Y)-box 5 isoform b
12 rs17309820 Nothing near
13 rs4770394 Nothing near
13 rs2806922 KIAA0853=znf protin?
13 rs12584910 Nothing near
14 rs221703 DHRS2=dehydrogenase/reductase (SDR family) member 2
14 rs17108400 FLJ43028 fis
14 rs17113911 Nothing near
14 rs10146912 KLHDC1=kelch domain containing 1
14 rs3784005 FLVCR2=feline leukemia virus subgroup C cellular
14 rs10438244 FLJ25257 fis,
15 rs7163502 TBC1D21=TBC1 domain family member 21
16 rs1420239 Nothing near
16 rs4567706 Nothing near
16 rs12149894 Nothing near
16 rs7184080 Nothing near
16 rs10220973 FLJ43761 fis
17 rs203466 AKAP10. A-kinase anchor protein 10 precursor
18 rs7243929 Nothing near
18 rs1893146 Nothing near
19 rs12979795 ZNF490
19 rs7408169 not found in database
19 rs2061332 ZNF224/ZNF225
19 rs7248493 ZNF274
20 rs4815603 CENPB, CDC25B
20 rs6031991 KCNS1=potassium voltage-gated channel
21 rs2833193 Nothing near
22 rs11089599 SYN3=synapsin III isoform IIIc
22 rs16997510 CSF2RB=colony stimulating factor 2 receptor beta

There are also a few interesting points about the list which were not properly covered by the paper: a number of the SNPs pick up the same four genes which are:

DPP10, dipeptidyl peptidase 10

TRDN, triadin1 calcium receptor interacting

DFNB31= CASK-interacting protein CIP98

CDC25B, cell division cycle 25B isoform 2

Even more exciting is that one of these genes, DFNB31, together with the GRM7 gene are to found in both GWA bipolar studies. The DFNB31 gene is especially provocative because it has been implicated in deafness/blindness previously. Hard to see how that relates to bipolar disorder until you realise that a skin condition and a form of deafness can be caused by the same gene AND when you read abstracts like this.

You heard it here first (actually, SRF has a broadly similar perspective) - perhaps these genes will be the Next Big Things in bipolar disorder genetics, surely a clear justification for Big Science.

And after the previous post, here is a negative view of DSM. Both articles fascinating for the non-clinician.

An interesting New Yorker article on the history of the DSM manual for diagnosing psychiatric illnesses - and the man most responsible for its creation.

Talking parrots, cursing Sumatran orang utans, accidental gene therapy on your family, biotech industrial espionage through cell line contamination, body-snatching, legally-enforced tissue sampling by bounty-hunters, illegal human-chimp hybrids etc. etc. These are some of the plot-points in ‘Next‘, Michael ‘Jurassic Park’ Crichton’s new book. Of late, Crichton has turned away from the straight techno-thriller to issue-based thrillers. His last book, ‘State of Fear‘ tackled the politics of climate change. In this book, the focus is the ethical dilemmas raised by the rapid progress made in the area of medical genetics.

Crichton needs a vent for his polemic and many of the characters in the book are merely poorly fleshed-out mouthpieces for his beliefs - a disappointment from the creator of ER, an accomplished character ensemble piece. In fact, Crichton’s fundamental anger at scientists and the ramifications of modern genetics clouds the book to such an extent that the only protagonists that the reader can begin to empathise with are of the non-human variety: the aforementioned wise-cracking African grey parrot and the faeces-tossing human-chimp hybrid! The human characters, especially the scientists, are portrayed as unscrupulous, money-grabbing, self-aggrandising monsters.

The very first page sets the scene for the pervasive criminality and moral bankruptcy among the scientists (and had particular resonance for me!):

“It wouldn’t be the first time a postdoc got tired of working on salary. Or the last.”

Now scientists are of course prone to all human frailties but Crichton tends to forget a) some might be doing their job because they believe it will do some good b) medical science has actually made tangible contributions to the modern world. He prefers to concentrate, Mary Shelley-style, on scientists as destroyers of the natural order or devious prospectors in a genetic goldrush. One gets the impression that Crichton has cut and pasted the merciless personalities of lawyers or financiers from his previous thrillers straight into this book.

His need to get his concerns across has meant that ‘Next’ is a book of two halves. In the first half, numerous bizarre vignettes and press clippings serve to dramatise the ethics of human genetic research and commercialisation. These are mixed with a multi-strand plot set-up for the latter half of the book, which follows a much more conventional, if rather weak, thriller structure. As such, Crichton tests the patience of the reader looking for the filmic flowing story that he normally produces.

However, if you can get beyond Crichton’s leaden writing style and sensational plotting, there are some interesting opinions to be found which have particular relevance to the research work carried out in psychiatric genetics. This is especially evident in a tagged-on chapter at the end in which he proposes five main changes which he believes will save medical genetics from itself (see below).

But before I discuss that, Crichton has some explaining to do…
Generally Crichton knows the science (he is a Harvard medical school graduate and directed ‘Coma’ which shares similar themes), although he does make a few howlers. These include the laughable (although perhaps suitably sensationalist) mislabelling of a transgenic chimp as a ‘transgender’ chimp and a misconception that human-chimp homology refers to genes rather than nucleotides (’humans have 500 different genes compared to chimps’)! But, more importantly, has Crichton chosen the right medium to voice his concerns? The problem I have is that the lay reader is ill-equipped to make the distinction between the outrageous actions of the portrayed scientists (the thriller) and the author’s calmly reasoned arguments set out in the book’s post-script. The former appears to be used as justification for the latter. Neither the standard procedural controls on scientific research nor the the typical motives of scientists are presented to the reader. Real research involving experimentation of any sort is regulated ad infinitum. In the UK for example, if you want to carry out an animal experiment you would, quite rightly, need a personal license, a project license and a site license…then you would have to convince a funding body that your research was ethically justifiable….all before you started…and then your procedures are monitored throughout: including vet inspections. In terms of real-world motives, scientists in UK academia are within a nation-wide pay-scale, with any consultancy work negotiated through (and capped by) the University. Aside from setting up spin-off companies, there are no opportunities for amassing vast personal wealth through fair means or foul. Scientists really want publications and money for research and that is the basis for much commercialisation of their findings…as leverage for funding from industry. In ‘Next’ we have scientists accidently taking viral gene therapy materials home in the car and infecting family members….impossible. We have scientist carrying out an apparently unfunded and unauthorised human-chimp hybridisation experiment as some sort of sabbatical afterthought…..in the full knowledge that it would be utterly unpublishable: in the Real World there would be no point, quite aside from the illegality of the procedure. So Crichton’s fiction and fact approach is a little dishonest if entertaining.

Apart from the attempt by a character to forcibly genetically test his wife for Bipolar Disorder as part of a custody battle, it’s not until the sober manifesto at the end of the book that there is much to debate for those working in psychiatric genetics. Readers should be aware that Crichton is writing with respect to US law and practice but there are many crossovers into more universal problems. His five points are:

1. Genes should not be patented
2. Tighter regulation on the use of human tissues
3. Full disclosure of gene therapy/drug testing data to the public
4. Remove all bans on particular aspects of genetic research (e.g. stem cells)
5. Rescind the Bayh-Dole act (reducing the ties between academia and industry….a US issue)

The first two points are particularly interesting. Crichton has issue with speculative gene patenting….as epitomised by Myriad Genetics’ actions. He presents some compelling examples where patents have hindered the pace of research relating to important public health issues. However, Crichton sees gene patenting as some sort of ‘people ownership’: the removal of some innate freedom of the individual. Not mentioned is the astronomical cost of new drug development and testing - a burden that only industry can realistically carry. It is simple economics that they want to protect that huge investment, and gene patenting is the first step to safeguarding intellectual property along the length of the pipeline leading to the new therapy. In this light, patenting can be thought of as advantageous to the individual - it’s the only viable model for the production of new medicines. Moreover, gone are the days of vague gene patents…now experimental evidence and a precisely defined scope is required to persuade patent assessors that a gene patent merits granting.

Tissue/patient samples are cause for concern because Crichton believes that the ‘ownership’ of the samples is not clearly defined in law: does the patient retain rights, the clinician or the academic body? Should the patients’ permission be sought if samples are to used for different research purposes. Crichton touches on the implications for family members should an individual be found to be genetically compromised. This point is going to be very important for psychiatric genetics in the next decade as a multitude of discovered genetic risk factors are identified and converted into diagnostic reagents. Who within and without the family circle should be privy to such information?

Given the public exposure of this book, scientists should be prepared not only to answer questions arising from it but also engage in the ongoing debate over changes in regulations and laws.

Reasons to be cheerful..

Too much to read….
Here’s a plot similar to the ones I produced in the very first post on this blog. It clearly shows the increase in publications in the past year relating to four of the most well-known genes in the context of either schizophrenia or bipolar disorder.

I think this reveals more about the high activity of the field than the candidacy of any one particular gene.

Supporters in high places…

From Google:

“James D. Watson, Nobel Laureate and Chancellor of Cold Spring Harbor Laboratory, describes the years leading up to his 1953 discovery of the structure of DNA, for which he won the Nobel Prize with Drs. Francis Crick and Maurice Wilkins (1962). Dr. Watson explains that the key to uncovering the causes of brain disorders such as schizophrenia, depression, fragile X syndrome, Alzheimers, etc. is in our genes. He depicts the strides being made by scientists at Cold Spring Harbor Laboratory, a research institution in the biological sciences, as they search to find the genetic basis of neurological disorders. CSHL scientists’ seach to root out disease genes related to mental illnesses, such as schizophrenia and autism and devise strategies for treating a variety of neurological disorders will be discussed. “
Click here for the speech (starts about 7 minutes into running time).

A new textbook for the new year

Here is a roughly collated list of the gene candidates for schizophrenia and bipolar disorder studied by labs across the World in 2006 (definitely not exhaustive: mostly taken from association study papers where the gene in question might have shown association or not).

5HTT
ADRA2A
AHI1
ALG9
ALK
APOD
APOE4
ARNTL
ASCT1
ASPM
ATRAR4R
BDNF
BRD1
C6ORF217
CALRETICULIN
CAPON
CC16
CD14
CHROMOGRANINA
CHI3L1
CHRFAM7A
CHRNA7
CHROMOGRANIN3
CLOCK
CNP
CNR1
CNTF
COMPLEXIN1
COMPLEXIN2
COMT
CYP1A2
DARPP-32
DAT
DGCR14
DGCR2
dihydropyrimidinase-related protein 2
DISC1
DPYSL2
DRD1
DRD2
DRD3
DRD4
DTNBP1
DUSP6
EPSIN4
ERBB4
FAT
FEZ1
FGF2
FGFR1
FKBP5
FOXP2
FZD3
G72/G30
GABRB2
GAD1
GAD67
GLYT2
GNAS1
GNPAT
GPR24
GPR50
GPR78
GRIA1
GRIK3
GRIK4
GRIK5
GRIN1
GRIN2A
GRIN2B
GRK3
GRM3
GSK3B
HTR2A
IL10
IL3
INPP1
KCNN3
KIF1
KPNA3
KPNB3
kynurenine 3-monooxygenase
MAP6
MIF
MNSOD
MPZL1/PZR
MTHFR1
NAPG
NCAM1
NDUFV2
NET
NEUROGENIN1
NEUROGRANIN
NEUROPLASTIN
NLGN4
NOGO
NOS3
NOTCH4
NPAS3
NRG1
NUMBL
OLIG2
P2RX7
PCDH11Y
PCM1
PDLIM5
PER3
PICK1
PIP5K2A
PLA2G4A
PLXNB3
PRODH
PSD95
QKI
RGS4
RLN
SCYA2
SERINE RACEMASE
SIAT8B
SLC6A4
SMARCA2
SOX10
SYNAPSIN-III
synaptogyrin 1
synaptotagmin XI
TAAR6
TCP1
TNFA
TNFB
TNXB
TPH1
TRPM2
UCP
VMAT1
XBP1
ZDHHC8

On the face of it we should be congratulating ourselves on the rate of progress in the field - so many candidates to hold up to funders/press/public as important contributors to disease.

But is this the real picture? Could this breadth of study be highlighting the rather clueless and arbitrary manner in which genes are selected for study….and the failure of the field to commit to systematic replication of positive results. Let’s face it, how many of the new genes discovered last year are going to be the subject of follow-on studies? Very few, I’m sure…which renders the initial findings as non-results. That’s a little hard perhaps but imagine where we could have been if the field had carried out the equivalent of ‘SETI at home’….distributed genotyping of a limited set of genes. We could be here digesting our mince pies in the warm afterglow of some definitive answers.

The real New Year downer is the total lack of identified mutations in these genes. P2RX7 is the only gene with a decent set of follow-up statistical work on its population-level mutation….the other 141 genes still have no nailed-down, bona-fide point mutations showing association with illness. This is astonishing and verging on the shameful, especially when it is considered that some of the BIG genes have extensive and expensive functional/structural and behavioural/cognitive work carried out on them without the genetics being anywhere near a cast-iron certainty. Like house prices, this situation is untenable in the long run so everyone should keep their fingers crossed that their favourite gene doesn’t become ‘negative equity’ in 2007.

My New Year hope is that labs will take the plunge into resequencing candidate genes fully in carriers of associated haplotypes. If we have the conviction to publish the association studies then we have to have the conviction that mutations are underlying our haplotypes. In my mind, resequencing projects should be the natural successors to the BIG-funded whole-genome association studies.

Let’s hope that 2007 doesn’t end with a turkey too.

The challenge
I’m open to comments from those that think the following isn’t correct…..
So you have a lab and DNA samples donated with consent from individuals diagnosed with a psychiatric illness and a matched group without illness. How would you go about telling us something about the genetics of psychiatric illness? You’d think the strategy would be already well worked out…a simple recipe of experimentation and analysis which would give you the answer.

To borrow from W. Somerset Maugham:

“There are three rules for cloning complex disease genes. Unfortunately, no one knows what they are.”

The problem is circular…until we have a few of these genes in the bag we won’t know the best way to get them. Here, ‘in the bag’ means proper mutations, not inferred involvement.

In this post, I will tell you what I think you should NOT do (the horse-flogging bit) and some suggestions on what you should do (the horses for courses bit) given the nature of your starting materials.

Don’t try this at home (or in the lab)

Genome-wide linkage studies….or more precisely, multiple family genome-wide linkage studies. In essence, linkage looks for regions of chromosomes which seem to always be found in family members who have the particular condition being studied. The implication is that these regions contain the faulty gene. The snag comes when you go beyond looking at a single family and instead ask what regions of chromosomes are linked with the disease in all families, or most…or even a detectable proportion. It hasn’t and doesn’t work when you go beyond the single family. An Aesop fable may help you see why (although it should be blindingly obvious).

The King of Clonia loved his orchard and the bountiful fruit it produced each year. It covered a sizeable area of his palace grounds and consisted of a multitude of fruiting tree species collected and nurtured from all over his Kingdom. One day, in a fit of ill-advised enthusiasm, he issued an edict to his court alchemists.

“Identify the purest essence of each individual fruit so that I might bottle them all as a gift to the Queen”.

The Alchemists debated over this task and finally came up with this solution. They would collect one example of each fruit, place it in a single barrel and grind them all up. Then they would extract, filter and fractionate the essences in one glorious process.

“That’s crazy”, cried one dissenting Alchemist, “you are just making the problem more difficult…how will you tell orange essence from apple or from plum?”.

“Fool!”, they replied witheringly, “we have the power to detect all the essences simultaneously…the court arithmetician has decreed it so”.

They failed of course, but as is the wont of those convinced by statistical models, they decided that scaling up was the answer.

“More fruit for the barrel!!” went out the command. “Still more fruit!!”, as they failed again.

The moral of the story is made apparent when the ‘fool’ took the fruit of a single, large tree and extracted the essence as required. Rather than praise his efforts, the other Alchemists accused him of plucking low-hanging fruit and producing an essence that was not relevant to the orchard as a whole.

“Your essence looks nothing like ours…and doesn’t even look like that you got from looking at another big tree”, they said.

“Exactly! That’s the point! Isn’t it good!”, he exclaimed excitedly.

“Actually, we think that means you have made a mistake. Or the problem is intractable. Or that fruit beetles must be invoked as the guardians of the essence and must be factored into future extractions”, the Alchemists said without a hint of irony.

The King placed all of them in a specially commissioned barrel and left them to stew in their own juices.
The end.

Are families bad? A graphical answer

No! They are good. But you have to know how to use them correctly to get any meaningful genetic information from them. It all comes down to my favourite phrase, ‘genetic architecture’. How many mutations are there for a disease in the population, how common are they in the population, how strong is each one’s effect and do they run in families or are they ’sporadic’? The graphs below are my attempt to illustrate a genetic model that tries to explain that all of these questions are, in fact, just flip-sides of the same coin….and what is more, this genetic model can tell us what analysis techniques we should use when given a particular DNA sample set.

This first graph shows that the amount of each mutation in the population (its frequency) is directly related to how ’strong’ its effect is (names like Odds Ratios are just genetics terms for measuring mutation effect strength). This is the critical concept which helps explain the rest. Common mutations have weak effects and rare mutations often have strong effects….and every combination in between - as shown by the wide yellow band.

The pretty obvious fact (you would think) is that if a mutation has a strong effect then it will be apparent in most individuals it is passed on to. Hence, it will be recorded as evidence of a family history. In terms of ascertainment bias (shiny things grab our attention), it will highlight a family, catching the eye of a physician collecting DNA samples for a gene-hunting exercise. Weak effect mutations will probably have to work together to push an individual down the route of illness - but that’s OK as they are generally common and likely to be inherited together by chance in unlucky people (note the use of inherited here…it is not a process any different from the nominally familial individuals). But these individuals won’t be part of the families - they will appear as ’sporadic’ cases, caused by the random convergence of population risk factors.

Methods are horses and DNA sets are courses

So this figure is the crunch. We have this model but how do we apply it? Well each experimental technique/method of data analysis has its strengths and weaknesses. I think that researchers should be very, very careful when looking at the DNA sample set in front of them and deciding what they want to do. If it is a familial heavy set (left-hand side of the graph) then you would want to do single large family genome-wide linkage studies, the related technique of following the co-segregation of candidate gene alleles and illness through a family or, finally, deep resequencing. This final technique is not really applied much as it requires the selection of a candidate gene and then sequencing this gene in many DNA samples from individuals with a family history. The idea is that you may be lucky and hit a rare, strong effect (’highly penetrant’ in genetic-speak) mutation.

If, on the other hand (right of the graph), you have a predominantly sporadic DNA sample set than this opens up the possibility of case-control association studies. This technique compares the frequency of candidate gene alleles (’flavours’ of the same gene) between people with illness and unaffected people. If a difference is seen then that gene allele indicates the nearby presence of a mutation. This approach requires the alleles to be reasonably common in the population otherwise they will not be detectable. Hence, case-control association studies and familial (rare allele) samples shouldn’t be combined…..if you want anything out the other end. This is entirely analagous to the multiple family genome-wide linkage problem….here the issue is the number of different fruit types combined in the barrel - you won’t taste the bad apple in your fruit smoothie.
We are entering the age of the whole-genome case-control association study where big-science does away with subjective choice of candidate genes and just screens everything. While the rather poor coverage of each gene (and its constituent LD blocks) might sometimes work against the aims of the experiment - a story for another time, perhaps - let’s hope that nobody applies the wrong DNA sample set and then looks puzzled when results are lacking……..

A gene for…

Barely a week goes by without a press release describing the identification of ‘a gene for X disorder’. But what does that mean exactly? Naturally, it doesn’t mean that the purpose of that gene is to cause the disorder - rather, damage to that gene (a mutation) directly causes, or increases the risk of, disorder X. The normal function of that gene, probably making a protein which has a role in some process in the cell, has been changed in a harmful way. Maybe the ‘encoded ‘ protein is not made, or its activity is changed.

Cases and controls

Schizophrenia and bipolar disorder are complex genetic disorders which means that each individual gene mutation is likely only to contribute to a small fraction of illness in the population (even if it can be a major contributory factor for particular affected individuals). That is why ‘case-control association studies’ are employed by researchers wishing to assess how important their particular gene of interest is in the general scheme of things. Basically, they use natural gene variants (sequence differences) that exist in the population and ask whether they are more common in individuals with the disorder (cases) compared to the well population (controls). If the variant is more common then either it, (or more likely) a mutation nearby in the gene, is responsible for predisposing some people towards developing the illness (the variant, and the gene in general, is said to be ‘associated’ with the illness).

Strange associations

Even though the title of this post is a little misleading, there have been some unexpected and provocative findings from association studies that have been treated with great suspicion (generally by those who review the data presented in scientific papers). In brief, some genes seem to contribute to illness in one ‘sex‘ only and other genes seem to offer ‘protection‘ against illness rather than contributing to it. Because the strength of these studies critically depends on the number or cases and controls used (the more, the better the study) and the mathematical analyses used, it is easy for those suspicious people mentioned above to dismiss these observations as mere ‘lies, damned lies and statistics’ - researchers trying to wring something of interest from their favourite gene.
If you don’t look, you won’t find…
Perhaps these findings are only a surprise because the older, and more widely used, method of gene hunting - ‘linkage analysis’ - is not well-equipped to find such associations. Firstly, to find a protective effect for a gene would require a linkage-using researcher to look for ‘well’ families - the opposite of normal - and ask why they were normal. Not a particularly precise approach - and one not likely to find financial support for investigation. [Having said that....] Linkage studies can also examine if an illness is passed down a maternal or paternal line (indicating processes such as imprinting, mitochondrial disease or sex-linkage…topics that I can’t do justice to here) but which gender is affected is not often monitored.

So the advent of widespread asociation studies has revealed these two peculiarities, but is there any biology to explain them? Let’s look at protective effects first.

Newtonian genetics

One explanation of the observation of protection is a simple mathematical counterpart of ‘an action causing an equal and opposite reaction’. Below, I’ve pasted in a real-world example of some data from a gene, NPAS3, we are working on (the data is being presented initially at the World Congress of Psychiatric Genetics in Sardinia this weekend).

One part of the gene has 6 principal variants (‘haplotypes’: 111, 121….etc.). The four columns for each variant represent how common each one is in healthy control individuals (CONT), individuals with schizophrenia (SCZ), individuals with bipolar disorder (BPD) and a combined group made up of both conditions (COMB). What you can see is that there is a big shift for the ‘212’, and to a lesser extent, ‘211’, variants. 212 appears to represent a ‘susceptibility’ variant (more common in cases) whereas 211 seems to be a ‘protective’ variant (lower in cases). But is it really? A more plausible explanation is that the 211 decrease is just a passive response to the 212 increase – something has to ‘give’ to compensate for more people being in the 212 group. If indeed this is the case, then the other variants would also show a similar (if proportional) drop. 122 fits this model nicely but the others are not so clear - so you can see how this is not always the easiest trend to spot. Incidentally, the SCZ and COMB groups for 212 show the most statistically significant p-values for this data set (an indication, perhaps, that they are the driving force in this shifting picture).

Real protection

A clearer picture of protection comes from the study of the genes GRIK4 and DISC1…… In the former case (link to review in the Schizophrenia Research Forum) there is a schizophrenia susceptibility region in the centre of the gene and a clear bipolar disorder protective variant (haplotype) at the end of the gene which is present in around 16% of individuals with bipolar disorder and about 23% of control individuals.

We are all schizophrenic…….

Let me propose a strange and unlikely situation where the forces driving the evolution of the human brain have led to schizophrenia (or bipolar disorder) becoming more prevalent….possibly even the default state. In a contemporaneous evolutionary arms race, gene variants would have appeared and been selected for their protective effect against schizophrenia. In this way a large set of protective variants might exist at relatively high frequencies in the population such that 99% of people would not develop the disorder.

This is an exaggeration to make the point that it would be theoretically possible to construct a genetic model of schizophrenia using only protective factors. However, despite its wackiness, there are a couple of concepts in complex genetic disorder-speak that seem to cry out for an acknowledged role for protective variants. First, there is ‘reduced penetrance’, which refers to the phenomenon where mutation carriers don’t always develop the full-blown disease. Something is compensating for the mutation. This ‘something’ is often described as ‘genetic background’ - a rather nebulous term meaning ‘a whole load of genetic (and maybe environmental) influences we cannot hope to quantify or understand’. Surely, it would be better to bite the bullet and admit that some of the protective variants we are observing could be active components of this background? Second, and closely related, is the concept of disease ‘threshold’ – susceptibility and, by implication, protective factors, are competing in a genetic tug-of-war. The net result is that the host human ends up on one side or other of the disease threshold.

The next phase of industrial scale genetic research into psychiatric disorders will involve the use of ‘whole genome associations’ – testing each gene simultaneously for its role in illness. I predict that people are going to be surprised at just how many gene variants are protective. I also predict that these variants might give us more of an idea of the biological strategies that could be adopted in the rational design of new therapies – essentially, we would be following Mother Nature’s lead.

Gender issues

If you find the idea of protective factors is rather outlandish, then the existence of gender-specific associations is going to be even harder to accept. The fact is that some gene variants only seem to alter the risk of illness when you look at just one sex in isolation. DISC1’s effect on both bipolar disorder and schizophrenia (Thomson et al 2005) and GPR50 on bipolar disorder (Thomson et al 2005) are both clear examples of this phenomenon. I think that some of the prejudice against the existence of sex-specific associations comes from the misapprehension that this doesn’t fit with the commonly quoted fact that schizophrenia and bipolar disorder affect both sexes equally. I would argue that, just as there are multiple susceptibility and protective variants, there are likely to be multiple male-specific and female-specific variants – the biases must average themselves out in the end. Having said that, my concern at present is that most sex-specific associations seem to be female in type.

What about biological mechanisms? There is an established and growing set of non-psychiatric genes that also possess sex-dependent risk variants (Weiss et al 2006). For example, a gain-of-function mutation with a sex-specific (and protective) effect against Parkinson Disease (Glatt et al 2006) and a sex-dependent risk polymorphism for non-familial Hirschsprung disease (Emison et al 2005) have both been recently described and go some way towards a functional explanation for such phenomena. Schizophrenia usually has its onset post-pubertally in teenage and early adult life in both sexes. As both these sex-specific examples result from regulatory polymorphisms, hormonal influences on transcriptional control can be postulated as an underlying mechanism – as documented for the cAMP response element binding (CREB) protein (Auger 2003, Abraham et al 2005, Zubenko et al 2003). In other words, a gene variant would exist in both sexes as per normal genetic rules, but it only has a biological consequence in one sex because of an altered interaction with some sex-hormone-linked process. Hence, a plausible strategy for future research would be a search for neuroendocrine-modulated intronic regulatory element polymorphisms in the DNA of carriers of sex-specific variants.

In summary, we have had a glimpse of some intriguing variations to the normal risk gene action for psychiatric illness. It will be interesting to see how these phenomena develop over the next few years.

Here’s a link to an article remembering early work on the self-administration of LSD in order to measure, and experience, the physiology and sensations of schizophrenia/psychosis.

Originally posted on digg.