Genes, brains and lateralisation: how solid is the evidence?

If there were a dictionary of famous neurological quotes, “Nous parlons avec l'hémisphère gauche” by Paul Broca (1865) would be up there among the top hits. Broca’s realisation that the two sides of the brain are functionally distinct was a landmark observation. It was based on a rather small series of patients, but has since been confirmed in numerous studies. After localised brain injury, aphasia (language impairment) is far more likely after damage to the left side than the right side. And nowadays, we can visualise greater activation of the left side in neurologically intact people as they do language tasks in a brain scanner.

There are many fascinating features of cerebral lateralisation, but I’m going to focus here on just one specific question: what do we know about genetic influences on brain asymmetry in humans?  There are really two questions here: (1) how do genes lead to asymmetric brain development? (2) are there genetic variants that can account for individual variation?  – e.g. the fact that a minority of people have right hemisphere language. I hope to return to question 2 at a later date, but for now, I’ll focus on question 1, because after reading a key paper on this topic, I've struck a whole load of questions that I can’t answer. I’m hoping that some of my genetically-sophisticated readers will be able to help me out.

It’s sometimes stated that cerebral lateralisation is a uniquely human trait, but that’s not true. Nevertheless, we are very different from our primate cousins, insofar as we show a strong population bias to right-handedness, and most people have left-hemisphere language. There are other species which show consistent brain asymmetries, but they are a long way from us on the evolutionary tree. Most of the research I’ve come across is on nematode worms, zebrafish, or songbirds. This is a long way from my comfort zone, but there are some nice reviews that document research on genes influencing asymmetries in these creatures (e.g. here and here). It’s clear, though, that it’s complicated: not just in terms of the range of genes involved, but in the different ways they can generate asymmetry. And there don't seem to be obvious parallels to human brain development.

Despite all this uncertainty, there’s growing evidence that brain asymmetries are present from very early on in life –in newborn babies and even in foetal life. This field is still in its infancy (forgive the pun), and samples of babies are typically too small to reveal reliable relationships between structure and function. Nevertheless, there’s considerable interest in the idea that physical differences between the two sides of the brain may be an indicator of potential for language development.

A particularly exciting topic is genetic determinants of cerebral lateralisation. One study in particular, by Sun et al made a splash when it was published in Science in 2005, since when it has attracted over 140 citations. The authors looked for asymmetric gene expression in post mortem embryonic brains. Their conclusions have been widely cited: “We identified and verified 27 differentially expressed genes, which suggests that human cortical asymmetry is accompanied by early, marked transcriptional asymmetries.” The fact that several different genes were identified was of particular interest to me, because genetic theories by neuropsychologists have typically assumed that just a single gene is responsible for human cerebral lateralisation. I’ve never found a single-gene theory plausible, so I was all too ready to accept evidence that involved multiple genes. But first I wanted to drill down deeper into the methods to find out how the authors reached their conclusions. I’m a psychologist, not a geneticist, and so this was rather challenging. But my deeper reading raised a number of questions.

Sun et al used a method called Serial Analysis of Gene Expression (SAGE) which compares gene expression in different tissues or – as in this case – in corresponding left and right regions of the embryonic brain. The analysis looks for specific sequences of 10 DNA base-pairs, or tags, which index particular genes. SAGE output consists of simple tables, giving the identity of each tag, its count (a measure of cellular gene expression) and an identifier and more detailed description of the corresponding gene. These tables are available for left and right sides for three brain regions (frontal, perisylvian and occipital) for 12- and 14-week old brains, and for perisylvian only for a 19-week-old brain. The perisylvian region is of particular interest because it is the brain region that will develop into the planum temporale, which has been linked with language development.  One brain at each age was used to create the set of SAGE tags.

To identify asymmetrically expressed genes the authors state performed a Monte Carlo test and verified this using the chi square test. I haven’t tracked down the specifics of the Monte Carlo test, which is part of the SAGE software package, but the chi square is pretty straightforward, and involves testing whether the distribution of expression on left and right is significantly different from the distribution of left vs. right expression across all tags in this brain region – which is close to 50%.  In the left-right perisylvian region of a 12-week-old embryonic human brain, there were 49 genes with chi square greater than 6.63 (p < .01): 21 were more highly expressed on the left and 28 more highly expressed on the right.  But for each region the authors considered several thousand tags. So I wondered whether the number of asymmetrically expressed genes was any different from what you’d expect if asymmetry was just arising by chance.

It was possible to check this out from the giant supplementary Excel files that accompany the paper, but this proved far from straightforward.  It turns out that the relationship between tags and genes is not one-to-one.  For around 40% of the tags, there is more than one corresponding gene. It was not clear which gene was selected in such cases, and why. I did find some cases where two genes were assigned to a tag, but my impression was that this was unintentional and in general the authors aimed to avoid double-counting tags. We also have the further problem that some genes are indexed by numerous tags, a point I will return to below.

But let’s just focus first on the individual tags. I compiled a master list of all tags that were expressed in any region at any age, and then made a chart of the frequency of expression in each brain region/age. I excluded any tags where the total expression count on both sides was three or less, as this is too small to show lateralisation, and this left me with 3800 to 4600 tags for analysis in each brain region. I did compute chi square as described by Sun et al, but this is not recommended for small numbers, and so I also evaluated the significance of asymmetry using a two-tailed binomial test. This doesn’t make a huge difference, but is more accurate when comparing small numbers.  Figure 1 shows the proportion of the sample for each brain region where the binomial test gives a p-value of a given size. If the distribution of expression in left and right was purely determined by chance, we’d expect the points to fall on the line. If there were genes for asymmetry we would expect the observed values to fall above the line, especially at low levels of p. It is clear this is not the case. I did cross-check my figures against those of Sun et al, and found they appeared to have missed some cases of significant asymmetry, which meant that in general they found rather fewer cases of significant asymmetry than are shown in Figure 1.

Fig 1. Proportion of tags with "significant" asymmetry, by Age/Brain Region

Sun et al didn’t rely solely on statistical tests of SAGE data to establish asymmetrical expression.  They reported validation studies using a different method for assessing gene expression (real-time PCR). But this used genes selected on the basis of a chi square value of 1.9 or greater (P < .17), which included many where the degree of asymmetry was not large. One goal of PCR analysis was to confirm asymmetric expression levels in the same embryonic brains as the SAGE analysis. Of more interest is whether the findings generalise to new brains. The authors did further cross-validation using real-time PCR with six additional brains of different ages, and reported results for the LMO4 gene, where higher perisylvian expression on the right was evident in two brains at 12 and 14 weeks of age, as well as in the original two brains of the same age. Four other brains, aged 16 to 19 months, did not show asymmetry of expression. Some of the other asymmetrically expressed genes were also tested using real-time PCR in the two other brains, and 27 showed consistent asymmetric expression. It was, however, not clear to me how the significance of asymmetry was assessed in these replication samples.

There is one particular issue I find confusing when I try to evaluate the robustness of the asymmetry results. My expectation was that if a gene was asymmetrically expressed, then this should be evident in all the tags indexing that gene. But Table 1 shows that this isn’t so. For the LMO4 gene, which is the focus of special attention in this paper, there are seven tags that are linked with the gene in at least one brain region: only one of these (in red) shows the rightward asymmetry that is the focus of the paper. Another tag (in blue) shows leftward asymmetry in one sample, and the rest have low levels of expression. Maybe there’s a simple explanation for this – if so I hope that expert geneticists among my readers may be able to comment on this aspect.

Table 1. Left- and right-expression levels for seven tags for the LMO4 gene

I’m aware of two other studies (here and here) that looked for asymmetric gene expression in embryonic human brains but failed to find it . One possible reason for this discrepancy is that these studies focused on later stages of development, rather than the 12-14 week-old period where Sun et al found asymmetry. In addition, power is always low in these studies because of the small number of brains available. As Lambert et al (2011) noted, as well as possible effects of age and gender, there may be individual variation from brain to brain, but typically only one or two samples are available at each age.

So what do I conclude from all of this? I realise for a start that these studies are very hard to do. I also realise we have to make a start somewhere, even if the amount of post mortem material is limited. But I have to say I’m not convinced from the evidence so far that the researchers have demonstrated significant asymmetry of genetic expression in embryonic brains. The methods seem to take insufficient account of the possibility of chance fluctuations in the measurements, and the numbers of asymmetries that have been found don't seem impressive, given the huge number of genes that were investigated. Clearly, something has to be responsible for the physical asymmetries that have been found in foetal and neonatal brains, and the odds seem high that genes are implicated. But is the evidence from Sun et al convincing enough to conclude that we have found some of those genes? I'd love to hear views from readers who have more expertise in this area of research.

P.S. 7th Jan 2013
Thanks to Silvia Paracchini, who drew my attention to further relevant articles:
Johnson, M. B., et al (2009). Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron, 62(4), 494-509. doi: 10.1016/j.neuron.2009.03.027
This paper looked at a slightly later developmental stage - 18 to 23 weeks gestational age - and did correct for the number of genes considered (False Discovery Rate). They reported striking symmetry of gene expression in the mid-gestational period, even though structural brain asymmetries have been described at this stage of development. Note, however, that this is not incompatible with Sun et al, who did not find evidence of asymmetry after 17 weeks gestational age.
Kang, H. J., et al (2011). Spatio-temporal transcriptome of the human brain. Nature, 478(7370), 483-489. 
This is a much larger study, covering the range from 4 weeks gestational age through childhood up to adulthood and old age. This paper does not explicitly report on asymmetry, but they describe genes where the expression varies from brain region to region, or from age to age, after adjustment for False Discovery Rate. I could find no overlap in the list of the genes identified by Sun et al and Kang et al's list of differentially expressed genes.

References 

Abrahams, B. S., Tentler, D., Peredely, J. V., Oldham, M. C., Coppola, G., & Geschwind, D. H. (2007). Genome-wide analyses of human perisylvian cerebral cortical patterning. Proceedings of the National Academy of Sciences, 104, 17849-17854.
 
Dehaene-Lambertz, G., Hertz-Pannier, L., & Dubois, J. (2006). Nature and nurture in language acquisition: anatomical and functional brain-imaging studies in infants. Trends in Neurosciences, 29, 367-373.
 
Kivilevitch, Z., Achiron, R., & Zalel, Y. (2010). Fetal brain asymmetry: in utero sonographic study of normal fetuses. American Journal of Obstetrics and Gynecology, 202(4). doi: 359.e1
10.1016/j.ajog.2009.11.001
 
Lambert, N., Lambot, M.-A., Bilheu, A., Albert, V., Englert, Y., Libert, F., . . . Vanderhaeghen, P. (2011). Genes expressed in specific areas of the human fetal cerebral cortex display distinct patterns of evolution. PLOS One, 6(3), e17753. doi: 10.1371/journal.pone.0017753
 
Lash, A. E., Tolstoshev, C. M., Wagner, L., Schuler, G. D., Strausberg, R. L., Riggins, G. J., & Altschul, S. F. (2000). SAGEmap: A public gene expression resource. Genome Research, 10(7), 1051-1060. doi: 10.1101/gr.10.7.1051
 
Sagasti, A. (2007). Three ways to make two sides: Genetic models of asymmetric nervous system development. Neuron, 55(3), 345-351. doi: 10.1016/j.neuron.2007.07.015
 
Sun T, Patoine C, Abu-Khalil A, Visvader J, Sum E, Cherry TJ, Orkin SH, Geschwind DH, & Walsh CA (2005). Early asymmetry of gene transcription in embryonic human left and right cerebral cortex. Science (New York, N.Y.), 308 (5729), 1794-8 PMID: 15894532

Sun, T., & Walsh, C. A. (2006). Molecular approaches to brain asymmetry and handedness. Nature Reviews Neuroscience, 7, 655-662.
 

Moderate drinking in pregnancy: toxic or benign?

There’s no doubt that getting tipsy while pregnant is a seriously bad idea. Alcohol is a toxin that can pass through the placenta to the foetus and cause damage to the developing brain.  For women who are regular heavy drinkers or binge drinkers, there is a risk that the child will develop foetal alcohol syndrome, a condition that affects physical development and is associated with learning difficulties.
But what of more moderate drinking? The advice is conflicting. Many doctors take the view that alcohol is never going to be good for the developing foetus and they recommend complete abstention during pregnancy as a precautionary measure. Others have argued, though, that this advice is too extreme, and that moderate drinking does not pose any risk to the child.

Last week a paper by Lewis et al was published in PLOS One providing evidence on this issue, and concluding that moderate drinking does pose a risk and should be avoided. The methodology of the paper was complex and it’s worth explaining in detail what was done.

The researchers used data from ALSPAC, a large study that followed the progress of several thousand British children from before birth. A great strength of this study is that information was gathered prospectively: in the case of maternal drinking, mothers completed questionnaires during pregnancy, at 18 and 32 weeks gestation.  Obviously, the data won’t be perfect: you have to rely on women to report their intake honestly, but it’s hard to see how else to gather such data without being overly intrusive. When children were 8 years old, they were given a standard IQ test, and this was the dependent variable in the study.

One obvious thing to do with the data would be to see if there is any relationship between amount drank in pregnancy and the child’s IQ. Quite a few studies have done this and a recent systematic review concluded that, provided one excluded women who drank more than 12 g (1.5 UK units) per day or who were binge-drinkers, there was no impact on the child. Lewis et al pointed out, however, that this is not watertight, because drinking in pregnancy is associated with other confounding factors. Indeed, in their study, the lowest IQs were obtained by children of mothers who did not drink at all during pregnancy. However, these mothers were also likely to be younger and less socially-advantaged than mothers who drank, making it hard to disentangle causal influences.

So this is where the clever bit of the study design came in, in the shape of mendelian randomisation. The logic goes like this: there are genetic differences between people in how they metabolise alcohol. Some people can become extremely drunk, or indeed ill, after a single drink, whereas others can drink everyone else under the table. This relates to variation in a set of genes known as ADH genes, which are clustered together on chromosome 4. If a woman metabolises alcohol slowly, this could be particularly damaging to the foetus, because alcohol hangs around in the bloodstream longer. There are quite large racial differences in ADH genes, and for that reason the researchers restricted consideration just to those of White European background. For this group, they showed that variation in ADH genes is not related to social background. So they had a very specific prediction: for women who drank in pregnancy, there should be a relationship between their ADH genes and the child’s outcome. However, if the woman did not drink at all, then the ADH genotype should make no difference. This is the result they reported. It’s important to be clear that they did not directly estimate the impact of maternal drinking on the child’s IQ: rather, they inferred that if ADH genotype is associated with child’s IQ only in drinkers, then this is indirect evidence that drinking is having an impact. This is a neat way of showing that there is an effect of a risk factor (alcohol consumption) avoiding the complications of confounding by social class differences.

Several bloggers, however, were critical of the study. Skeptical Scalpel noted that the effect on IQ was relatively small and not of clinical significance. However, in common with some media reports, he seems to have misunderstood the study and assumed that the figure of 1.8 IQ points was an estimate of the difference between drinkers and abstainers – rather than the effect of ADH risk alleles in drinkers (see below). David Spiegelhalter pointed out that there was no direct estimate of the size of the effect of maternal alcohol intake. Indeed, when drinkers and non-drinkers were directly compared, IQs were actually slightly lower in non-drinkers. Carl Heneghan also commented on the small IQ effect size, but was particularly concerned about the statistical analysis, arguing that it did not adjust adequately for the large number of genetic variants that were considered.

Should we dismiss effects because they are small? I’m not entirely convinced by that argument. Yes, it’s true that IQ is not a precise measure: if an individual child has an IQ of 100, there is error of measurement around that estimate so that the 95% confidence interval is around 95-105 (wider still if a short form IQ is used, as was the case here). This measurement error is larger than the per-allele effects reported by Lewis et al., but they were reporting means from very large numbers of children. If there are reliable differences between these means, then this would indicate a genuine impact on cognition, potentially as large as 3.5 IQ points (for those with four rather than two risk alleles). Sure, we should not alarm people by implying that moderate drinking causes clinically significant learning difficulties, but I don’t think we should just dismiss such a result. Overall cognitive ability is influenced by a host of risk factors, most of which are small, but whose effects add together. For a child who already has other risks present, even a small downwards nudge to IQ could make a difference.

But what about Heneghan’s concern about the reliability of the results? This is something that also worried me when I scrutinised Table 1, which shows for each genetic locus the ‘per allele’ effect on IQ. I’ve plotted the data for child genotypes in Figure 1. Only one SNP (#10) seems to have a significant effect on child IQ. Yet when all loci were entered into a stepwise multiple regression analysis, no fewer than four child loci were identified as having a significant effect. The authors suggested that this could reflect interactions between genes that are on the same genetic pathway.

Effect of child SNP variants (per allele) on IQ (in IQ points), with 95% CI, from Lewis et al Table 1,

I had been warned about stepwise regression by those who taught me statistics many years ago. Wikipedia has a section on Criticisms, noting that results can be biased when many variables are included as predictors. But I found it hard to tell just how serious a problem this was. When in doubt, I find it helpful to simulate data, and so that is what I did in this case, using a function in R that generates multivariate normal data. So I made a dataset where there was no relationship between any of 11 variables – ten of which were designated as genetic loci, and one as IQ. I then ran backwards stepwise regression on the dataset. I repeated this exercise many times, and was surprised at just how often spurious associations of IQ with ‘genotypes’ was seen (as described here). I was concerned that this dataset was not a realistic simulation, because the genotype data from Lewis et al consisted of counts of how many uncommon alleles there were at a given locus (0, 1 or 2 – corresponding to aa, aA or AA, if you remember Mendel’s peas). So I also simulated that situation from the same dataset, but actually it made no difference to the findings. Nor did it make any difference if I allowed for correlations between the ‘genotypes’. Overall, I came away alarmed at just how often you can get spurious results from backwards stepwise regression – at least if you use the AIC criterion that is the default in the R package.

Lewis et al did one further analysis, generating an overall risk score based on the number of risk alleles (i.e. the version of the gene associated with lower IQ) for the four loci that were selected by the stepwise regression. This gave a significant association with child IQ, just in those who drunk in pregnancy: mean IQ was 104.0 (SD 15.8) for those with 4+ risk alleles, 105.4 (SD = 16.1) for those with 3 risk alleles and 107.5 (SD = 16.3) for those with 2 or less risk alleles. However, I was able to show very similar results from my analysis of random data: the problem here is that in a very large sample with many variables some associations will emerge as significant just by chance, and if you then select just those variables and add them up, you are capitalising on the chance effect.

One other thing intrigued me. The authors made a binary divide between those who reported drinking in pregnancy and those who did not. The category of drinker spanned quite a wide range from those who reported drinking less than 1 unit per week (either in the first 3 months or at 32 weeks of pregnancy) up to those who reported drinking up to 6 units per week. (Those drinking more than this were excluded, because the interest was in moderate drinkers). Now I’d have thought there would be interest in looking more quantitatively at the impact of moderate drinking, to see if there was a dose-response effect, with a larger effect of genotype on those who drank more. The authors mentioned a relevant analysis where the effect of genotype score on child IQ was greater after adjustment for amount drank at 32 weeks of pregnancy, but it is not clear whether this was a significant increase, or whether the same was seen for amount drank at 18 weeks. In particular, one cannot tell whether there is a safe amount to drink from the data reported in this paper. In a reply to my comment on the PLOS One paper, the first author states: “We have since re-run our analysis among the small group of women who reported drinking less than 1 unit throughout pregnancy and we found a similar effect to that which we reported in the paper.” But that suggests there is no dose-response effect for alcohol: I’m not an expert on alcohol effects, but I do find it surprising that less than one drink per week should have an effect on the foetal brain – though as the author points out, it’s possible that women under-reported their intake.

I’m also not a statistical expert and I hesitate to recommend an alternative approach to the analysis, though I am aware that there are multiple regression methods designed to avoid the pitfalls of stepwise regression. It will be interesting to see whether, as predicted by the authors, the genetic variants associated with lower IQ are those that predispose to slow alcohol metabolism. At the end of the day, the results will stand or fall according to whether they replicate in an independent sample.


Reference
Lewis SJ, Zuccolo L, Davey Smith G, Macleod J, Rodriguez S, Draper ES, Barrow M, Alati R, Sayal K, Ring S, Golding J, & Gray R (2012). Fetal Alcohol Exposure and IQ at Age 8: Evidence from a Population-Based Birth-Cohort Study. PloS one, 7 (11) PMID: 23166662