Friday, 11 January 2013

Genetic variation and neuroimaging: some ground rules for reporting research

Those who follow me on Twitter may have noticed signs of tetchiness in my tweets over the past few weeks. In the course of writing a review article, I’ve been reading papers linking genetic variants to language-related brain structure and function. This has gone more slowly than I expected for two reasons. First, the literature gets ever more complicated and technical: both genetics and brain imaging involve huge amounts of data, and new methods for crunching the numbers are developed all the time. If you really want to understand a paper, rather than just assuming the Abstract is accurate, it can be a long, hard slog, especially if, like me, you are neither a geneticist nor a neuroimager. That’s understandable and perhaps unavoidable. The other reason, though, is less acceptable. For all their complicated methods, many of the papers in this area fail to tell the reader some important and quite basic information. This is where the tetchiness comes in. Having burned my brains out trying to understand what was done, I then realise that I have no idea about something quite basic like the sample size. The initial assumption is that I’ve missed it, and so I wade through the paper again, and the Supplementary Material, looking for the key information. Only when I’m absolutely certain that it’s not there, am I reduced to writing to the authors for the information. So this is a plea – to authors, editors and reviewers. If a paper is concerned with an association between a genetic variant and a phenotype (in my case the interest is in neural phenotypes, but I suspect this applies more widely) then could we please ensure that the following information is clearly reported in the Methods or Results section

1. What genetic variant are we talking about? You might think this is very simple, but it’s not: for instance, one of the genes I’m interested in is CNTNAP2, which has been associated with a range of neurodevelopmental disorders, especially those affecting language. The evidence for a link between CNTNAP2 and developmental disorders comes from studies that have examined variation in single-nucleotide polymorphisms or SNPs. These are segments of DNA that are useful in revealing differences between people because they are highly variable. DNA is composed of four bases, C, T, G, and A in paired strands. So for instance, we might have a locus where some people have two copies of C, some have two copies of T, and others have a C and a T. SNPs are not  necessarily a functional part of the gene itself – they may be in a non-coding region, or so close to a gene that variation in the SNP co-occurs with variation in the gene. Many different SNPs can index the same gene. So for CNTNAP2, Vernes et al (2008)tested 38 SNPs, ten of which were linked to language problems. So we have to decide which SNP to study – or whether to study all of them. And we have to decide how to do the analysis. For instance, SNP rs2710102 can take the form CC, CT or TT. We could look for a dose response effect (CC < CT < TT) or we could compare CC/CT with TT, or we could compare CC with CT/TT. Which of these we do may depend on whether prior research suggests the genetic effect is additive or dominant, but for brain imaging studies grouping can also be dictated by practical considerations: it’s usual to compare just two groups and to combine genotypes to give a reasonable sample size. If you’ve followed me so far, and you have some background in statistics, you will already be starting to see why this is potentially problematic. If the researcher can select from ten possible SNPs, and two possible analyses, the opportunities for finding spuriously ‘significant’ results are increased. If there are no directional predictions – i.e. we are just looking for a difference between two groups, but don’t have a clear idea of what type of difference will be associated with ‘risk’ – then the number of potentially ‘interesting’ results is doubled.
For CNTNAP2, I found two papers that had looked at brain correlates of SNP rs2710102. Whalley et al (2011) found that adults with the CC genotype had different patterns of brain activation from CT/TT individuals. However, the other study, by Scott-van Zeeland et al (2010), treated CC/CT as a risk genotype that was compared with TT. (This was not clear in the paper, but the authors confirmed it was what they did).
 Four studies looked at another SNP - rs7794745, on the basis that an increased risk of autism had been reported for the T allele in males. Two of them (Tan et al, 2010; Whalley et al, 2010) compared TT vs TA/AA and two (Folia et al, 2011; Kos et al, 2012) compared TT/TA with AA. In any case, the ground is rather cut from under the feet of these researchers by a recent failure to replicate an association of this SNP with autism (Anney et al, 2012).

2. Who are the participants? It’s not very informative to just say you studied “healthy volunteers”. There are some types of study where it doesn’t much matter how you recruited people. A study looking at genetic correlates of cognitive ability isn’t one of them. Samples of university students, for instance, are not representative of the general population, and aren’t likely to include many people with significant language problems.

3. How many people in the study had each type of genetic variant? And if subgroup analyses are reported, how many people in each subgroup had each type of genetic variant? I've found that papers in top-notch journals often fail to provide this basic information.
Why is this important? For a start, likelihood of showing significant activation of a brain region will be affected by sample size. Suppose you have 24 people with genotype A and 8 with genotype B. You find significant activation of brain region X in those with genotype A, but not for those with genotype B. If you don’t do an explicit statistical comparison of groups (you should - but many people don’t) you may be misled into concluding that brain activation is defective in genotype B – when in fact you just have low power to detect effects in that group because it is so small.
In addition, if you don’t report the N, then it’s difficult to get an idea of the effect size and confidence interval for any effect that is reported. The reasons why this is optimal are well-articulated here. This issue has been much discussed in psychology, but seems not to have permeated the field of genetics, where reliance on p-values seems the norm. In neuroimaging it gets particularly complicated, because some form of correction for ‘false discovery’ will be applied when multiple comparisons are conducted. It’s often hard to work out quite how this was done, and you can end up staring at a table that shows brain regions and p-values, with only a vague idea of how big a difference there actually is between groups.
 Most of the SNPs that are being used in brain studies are ones that were found to be associated with a behavioural phenotype in large-scale genomic studies where the sample size would include hundreds if not thousands of individuals, so small effects could be detected. Brain-based studies often use sample sizes that are relatively small, but some of them find large, sometimes very large, effects. So what does that mean? The optimistic interpretation is that a brain-based phenotype is much closer to the gene effect, and so gives clearer findings. This is essentially  the argument used by those who talk of ‘endophenotypes’ or ‘biomarkers’. There is, however, an alternative, and much more pessimistic view, which is that studies linking genotypes with brain measures are prone to generate false positive findings, because there are too many places in the analysis pipeline where the researchers have opportunities to pick and choose the analysis that brings out the effect of interest most clearly. Neuroskeptic has a nice blogpost illustrating this well-known problem in the neuroimaging area; matters are only made worse by uncertainty re SNP classification (point 1).
A source of concern here is the unpublishability of null findings. Suppose you did a study where you looked at, say, 40 SNPs and a range of measures of brain structure, covering the whole brain. After doing appropriate corrections for multiple comparisons, nothing is significant. The sad fact is that your study is unlikely to find a home in a journal. But is this right? After all, we don’t want to clutter up the literature with a load of negative results. The answer depends on your sample size, among other things. In a small sample, a null result might well reflect lack of statistical power to detect a small effect. This is precisely why people should avoid doing small studies: if you find nothing, it’s uninterpretable. What we need are studies that allow us to say with confidence whether or not there is a significant gene effect.

4. How do the genetic/neuroimaging results relate to cognitive measures in your sample?  Your notion that ‘underactivation of brain area X’ is an endophenotype that leads to poor language, for instance, doesn’t look very plausible if people who have such underactivation have excellent language skills. Out of five papers on CNTNAP2 that I reviewed, three made no mention of cognitive measures, one gathered cognitive data but did not report how it related to genotype or brain measures, and only one provided some relevant, though sketchy, data.

5. Report negative findings. The other kind of email I’ve been writing to people is one that says – could you please clarify whether your failure to report on the relationship between X and Y was because you didn’t do that analysis, or whether you did the analysis but failed to find anything. This is going to be an uphill battle, because editors and reviewers often advise authors to remove analyses with nonsignificant findings. This is a very bad idea as it distorts the literature.

And last of all....
A final plea is not so much to journal editors as to press officers. Please be aware that studies of common SNPs aren't the same as studies of rare genetic mutations. The genetic variants in the studies I looked at were all relatively common in the general population, and so aren't going to be associated with major brain abnormalities. Sensationalised press releases can only cause confusion:
This release on the Scott van-Zeeland (2010) study described neuroimaging findings from  CNTNAP2 variants that are found in over 70% of the population. It claims that: 
  • “A gene variant tied to autism rewires the brain"
  • "Now we can begin to unravel the mystery of how genes rearrange the brain's circuitry, not only in autism but in many related neurological disorders."
  • “Regardless of their diagnosis, the children carrying the risk variant showed a disjointed brain. The frontal lobe was over-connected to itself and poorly connected to the rest of the brain”
  • "If we determine that the CNTNAP2 variant is a consistent predictor of language difficulties, we could begin to design targeted therapies to help rebalance the brain and move it toward a path of more normal development."
Only at the end of the press release, are we told that "One third of the population [sic: should be two thirds] carries this variant in its DNA. It's important to remember that the gene variant alone doesn't cause autism, it just increases risk." 

Anney, R., Klei, L., Pinto, D., Almeida, J., Bacchelli, E., Baird, G., . . . Devlin, B. . Individual common variants exert weak effects on the risk for autism spectrum disorders. Human Molecular Genetics, 21(21), 4781-4792. doi: 10.1093/hmg/dds301(2012)
V. Folia, C. Forkstam, M. Ingvar, P. Hagoort, K. M. Petersson, Implicit artificial syntax processing: Genes, preference, and bounded recursion. Biolinguistics 5,  (2011).

M. Kos et al., CNTNAP2 and language processing in healthy individuals as measured with ERPs. PLOS One 7,  (2012).
Scott-Van Zeeland, A., Abrahams, B., Alvarez-Retuerto, A., Sonnenblick, L., Rudie, J., Ghahremani, D., Mumford, J., Poldrack, R., Dapretto, M., Geschwind, D., & Bookheimer, S. (2010). Altered Functional Connectivity in Frontal Lobe Circuits Is Associated with Variation in the Autism Risk Gene CNTNAP2 Science Translational Medicine, 2 (56), 56-56 DOI: 10.1126/scitranslmed.3001344

G. C. Tan, T. F. Doke, J. Ashburner, N. W. Wood, R. S. Frackowiak, Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2. Neuroimage 53, 1030 (2010).

Vernes, S. C., Newbury, D. F., Abrahams, B., Winchester, L., Nicod, J., Groszer, M., . . . Fisher, S.  A functional genetic link between distinct developmental language disorders. New England Journal of Medicine, 359, 2337-2345. (2008).

H. C. Whalley et al., Genetic variation in CNTNAP2 alters brain function during linguistic processing in healthy individuals. Am. J. Med. Genet. B 156B, 941 (2011).


  1. This is an interesting post, and I think reflects the desire to mix genetics with "Study X" (in this case, neuroimaging) and doing it posthoc (ie, on the samples you have) rather than thinking through this.

    One of the gotchas in genetics is that the vast majority of alleles have very low allele frequency - ie, they are rare in the population (5% is really "quite common" for a genetic allele). This is predicted from theory (as all variants must originally start out as just a singleton in the entire population) and is consistently observed, but it means you need more samples than you expect from other fields in genetics. This is because your minor allele - at say a reasonable 10% - is low; if you want to see people who are homozygous minor (both alleles minor) that's only 1% of your population.

    I blogged about this here as only recently have I really appreciated this:

    In answer to your point about genetics only showing Pvalues, a good genetics paper talks about the N (sample size) very prominently.

    The rough rule of thumb is:

    If your test can be localised to one place in the genome, eg, it is something where you know you are only interested in this region due to teh expression of a gene, then N= 100 (or perhaps 60) is ok.

    If you need to do something genomewide, then N=1000 (perhaps 500 for a good endophenotype)

    As every I think the right thing is to collaborate with a good scientist on the other side of the field, and also to think about this up front - the sample size is demanding, so you might want to (for example) do genotype-based phenotyping, (where you genotype say 200 people, but then phenotype a specific 20 subset - now selected for allele structure of interest) - but this obviously is predicated on you being confident about the genetics.

  2. Should read spot on Dorothy, pressed the button too early.

  3. I have been curious lately about very early childhood development and am interested in finding out what extent of genetic variation there is in brain anatomy of healthy individuals... I stumbled onto this article and thought maybe you could point me in the right direction.

    1. There are twin studies that can be used to estimate genetic effects. Here are a couple of references to those:
      Lenroot, R. K., Schmitt, J. E., Ordaz, S. J., Wallace, G. L., Neale, M. C., Lerch, J. P., . . . Giedd, J. N. (2009). Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Human Brain Mapping, 30(1), 163-174.
      Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S., & Poll, H. E. H. (2007). Genetic influences on human brain structure: A review of brain imaging studies in twins. Human Brain Mapping, 28(6), 464-473. doi: 10.1002/hbm.20398