SEARCH

SEARCH BY CITATION

Keywords:

  • BDNF;
  • cognition;
  • cognitive indicators;
  • genetic association;
  • rs6525;
  • Val66Met

Abstract

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

The Val66Met, G196A (rs6265) polymorphism in the brain-derived neurotrophic factor gene, BDNF, located at 11p13, has been associated with a wide range of cognitive functions. Yet, the pattern of results is complex and conflicting. In this study, we conducted a meta-analysis that included 23 publications containing 31 independent samples comprised of 7095 individuals. The phenotypes that were examined in this analysis covered a wide variety of cognitive functions and included indicators of general cognitive ability, memory, executive function, visual processing skills and cognitive fluency. The meta-analysis did not establish significant genetic associations between the Val66Met polymorphism and any of the phenotypes that were included.

The last decade has yielded a myriad of studies attempting to investigate connections between specific genes (or, more often, specific genetic variants) and complex behaviour traits. Five characteristics of these types of studies appear to be common. First, the results of such studies are often inconsistent, which raises the question of the replicability of these results. Second, individually, they are typically characterized by relatively small sample sizes. Third, there is a noticeable variability in the phenotypes utilized in this research. Fourth, there is relatively little ethnic diversity among the studied samples, with the overwhelming majority of them being sampled from populations of European descent. Finally, the field has been focused on a relatively small number of genes (or variants within these genes), whose proteins appear to be functionally highly important and highly pleiotropic, thus generating voluminous research depicting complex connections between these genes/variants and multiple behaviour traits.

One such prime candidate gene that, due to its function, has generated much attention in the field is the gene encoding the brain-derived neurotrophic factor (the BDNF gene), located at 11p13 (Houlihan et al. 2009). BDNF belongs to the neurotrophin superfamily (Matsuo et al. 2009), members of which include growth factors that play a role in the proliferation, differentiation and fate of neuronal cells and, consequently, in the regulation of synaptic plasticity and connectivity in the brain. BDNF is highly expressed in the central nervous system and is known to educe a plethora of functions, particularly in the hippocampus (Lau et al. 2010). It has also been shown that BDNF impacts the function of the key neurotransmitter systems: dopaminergic (Guillin et al. 2001), glutamatergic (Carvalho et al. 2008) and serotonergic (Mossner et al. 2000). Aberrations in BDNF function are associated with numerous neuropsychiatric disorders, although these associations are not consistent (Gong et al. 2009; Strauss et al. 2004; Verhagen et al. 2008, 2010). Moreover, variation in the BDNF genes has been studied as a source of individual differences in such traits as brain structure (Karnik et al. 2010) and function – e.g. as ascertained through electroencephalogram (Beste et al. 2010) and functional magnetic resonance image (Soliman et al. 2010) – intelligence (Hansell et al. 2007) and personality (Lang et al. 2005).

BDNF is synthesized as a precursor protein (proBDNF) that is proteolytically cleaved to produce mature BDNF (Greenberg et al. 2009). These two different types of BDNF preferentially bind to functionally different groups of receptors (Lee et al. 2001; Teng et al. 2005); proBDNF is bound by p75NTR– a member of the tumour necrosis factor receptor family, and mature BDNF is bound by the TrkB receptor tyrosine kinase. The former triggers biochemical reactions leading to apoptosis, axonal retraction and pruning dendritic spines (Roux & Barker 2002); the latter affects the cell cycle, neurite outgrowth and synaptic plasticity (Chao et al. 2006; Cowley et al. 1994; Mazzucchelli et al. 2002; Rosenblum et al. 2002).

The human BDNF gene is complex; it contains 10 exons that are alternatively spliced encoding for >30 mRNA transcripts (Pruunsild et al. 2007). BDNF is differentially expressed in diverse tissues and, within the brain, in various regions (Bishop et al. 1994; Timmusk et al. 1993). Moreover, transcript expression can be altered by environmental factors, such as stress (Fuchikami et al. 2009; Marmigere et al. 2003), so that the ratio of pro/mature BDNF may change (Tognoli et al. 2010). In addition to multiple isoforms, the gene has numerous polymorphisms. One of these polymorphisms is the G196A polymorphism (rs6265) in the proregion of BDNF resulting in a substitution of a valine for methionine residue at position 66 (Val66Met). It has been reported as having a variable frequency of the derivative (A) allele ranging from 0 or near 0 in Africa to up to 60% in Asia, and a frequency of 17% in Caucasians (www.ncbi.nlm.nih.gov). This polymorphism modifies the intracellular packaging of proBDNF and impacts activity-dependent secretion of the mature BDNF (Chen et al. 2004). The molecular and cellular characteristics of this polymorphism have been investigated in a number of in vitro and in vivo model systems, showing that the Val66Met differentially impacts BDNF availability, neuronal survival and morphology and altered neuronal function (Frielingsdorf et al. 2010). In humans, this polymorphism has also been extensively studied in association with a variety of cognitive traits in a number of different typical and disordered samples. It is the purpose of this article to review the findings obtained in association studies of the Val66Met and human complex cognitive traits and subject them to a meta-analysis.

Materials and methods

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Literature search

The primary literature search was conducted up to the end of December 2010 using the Medline, PsycINFO and Academic Search Premier research databases. The search terms that were used were ‘BDNF’, ‘brain-derived neurotrophic factor’, ‘intelligence’, ‘cognition’ and ‘memory’ in various combinations and using a number of the search options in the aforementioned databases.

Selection of literature and data extraction

The retrieved literature was examined for extractable data. The criteria for inclusion in the analysis were that the articles contain genotypic information as well as the means and standard deviations of various cognitive measures used in the published reports. Also extracted were the participants' country of origin, the number of participants in each study, the proportion of male and female subjects, the clinical status (and diagnosis) of the participants and the mean age of the participants. If an article did not have extractable data, the authors were contacted and this information was requested; all but one author released their data in a format that allowed for its inclusion in the analysis. There were 23 articles with 31 independent samples; all these publications were included in the meta-analysis. When articles contained more than one sample, each sample was treated as an independent sample. Similarly, when articles contained reports on more than one phenotypic category (see the following paragraphs), the indicators were treated as independent. Papers that included unknown cognitive tasks or data in formats other than means and standard deviations were excluded.

Phenotypic categories

After the extraction of the data was completed, all of the measures used in the literature were presented to a group of clinical and developmental psychologists (n∼ 20). These psychologists grouped all of the measures and reached consensus on the groupings, which resulted in the establishment of the five phenotypic categories. This process was completed twice to ensure reliability. The agreement between the first and second sorting was over 90%. The resulting phenotypic categories were general cognitive ability, memory, executive function, visual processing skills and cognitive fluency. Each of these phenotypic categories included a variety of assessments. A full listing of these assessments can be found in the legends for Figs. 1–5. Only one assessment indicator from each sample was included in the analysis of each phenotypic category. When there was more than one indicator reported that fit into a phenotypic category, the phenotype that appeared most frequently in the literature was selected.

image

Figure 1. Indicators of general cognitive ability. Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of this study in the analysis; lines represent the CI for the finding of this study; arrow represents the findings that are beyond the scale set for this figure; and diamond represents the overall effect of the meta-analysis. Unspecified IQ test: 1a, 1b, 1c (Egan et al. 2003); the Wechsler Adult Intelligence Scale: 2 (Tsai et al. 2004), 3 (Oroszi et al. 2006), 4a, 4b (Ho et al. 2006), 5a, 5b (Cerasa et al. 2010), 6a, 6b (Chung et al. 2010); the Wechsler Intelligence Scale for children: 7 verbal IQ (Strauss et al. 2004); test of nonverbal intelligence: 8 (Echeverria et al. 2005); Kaufman brief intelligence test: 9 (Erickson et al. 2008); Heim intelligence test: 10 (Miyajima et al. 2008a); estimated IQ (by spot word test): 11 (Schofield et al. 2009); cognitive abilities screening instrument, Chinese version: 12 (Tsai et al. 2008); g factor: 13 (Houlihan et al. 2009); mini mental state examination: 14 (Nacmias et al. 2004), 15a, 15b (Yu et al. 2008); problem solving: 16 (Ho et al. 2007); and national adult reading test: 17 (Baig et al. 2010).

Download figure to PowerPoint

image

Figure 2. Indicators of memory. Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of this study in the analysis; lines represent the CI for the finding of this study; arrow represents the findings that are beyond the scale set for this figure; and diamond represents the overall effect of the meta-analysis. Working memory: 1a, 1b, 1c (Egan et al. 2003); Wechsler Memory Scale: 2 (Strauss et al. 2004), 3 (Tan et al. 2005), 4 (Harris et al. 2006), 5a, 5b (Ho et al. 2006); digit span: 6 (Nacmias et al. 2004), 7 (Echeverria et al. 2005), 8a, 8b (Yu et al. 2008), 9 (Houlihan et al. 2009), 10a, 10b (Cerasa et al. 2010); verbal memory: 11 (Ho et al. 2007); delayed free recall: 12 (Oroszi et al. 2006); Rey–Kim assessment (a Korean memory assessment): 13a, 13b (Chung et al. 2010); and long delay: 14 (Tsai et al. 2008), 15 (Cathomas et al. 2010).

Download figure to PowerPoint

image

Figure 3. Indicators of executive functioning. Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of this study in the analysis; lines represent the CI for the finding of this study; arrow represents the findings that are beyond the scale set for this figure; and diamond represents the overall effect of the meta-analysis. Wisconsin card storing task: 1 (Rybakowski et al. 2003), 2a, 2b, 2c (Rybakowski et al. 2006), 3a, 3b (Cerasa et al. 2010), 4a, 4b (Chung et al. 2010); trails: 5 (Echeverria et al. 2005), 6 (Oroszi et al. 2006), 7a, 7b (Yu et al. 2008); attention: 8a, 8b (Ho et al. 2006), 9 (Ho et al. 2007), 10 (Tsai et al. 2008); and information–memory–concentration test: 11 (Nacmias et al. 2004).

Download figure to PowerPoint

image

Figure 4. Indicators of visual phenotype. Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of this study in the analysis; lines represent the CI for the finding of this study; arrow represents the findings that are beyond the scale set for this figure; and diamond represents the overall effect of the meta-analysis. Rey–Osterrieth complex figure test: 1 (Strauss et al. 2004), 2 (Oroszi et al. 2006), 3a, 3b (Ho et al. 2006), 4 (Ho et al. 2007), 5a, 5b (Yu et al. 2008), 6a, 6b (Cerasa et al. 2010), 7a, 7b (Chung et al. 2010); copying drawing: 8 (Nacmias et al. 2004); visual reproduction/construction: 9 (Echeverria et al. 2005), 10 (Tsai et al. 2008); and trials correct (i.e. correct identification of a visual target in a computerized visuospatial delayed-response task): 11(Hansell et al. 2007).

Download figure to PowerPoint

image

Figure 5. Indicators of cognitive fluency. Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of this study in the analysis; lines represent the CI for the finding of this study; arrow represents the findings that are beyond the scale set for this figure; and diamond represents the overall effect of the meta-analysis. Letter fluency: 1 (Nacmias et al. 2004), 2 (Miyajima et al. 2008a); vocabulary/verbal fluency: 3 (Echeverria et al. 2005), 4 (Harris et al. 2006), 5 (Houlihan et al. 2009); list-generating fluency: 6 (Tsai et al. 2008); and category fluency: 7a, 7b (Yu et al. 2008).

Download figure to PowerPoint

Analyses

Genotypes were grouped according to the presence or absence of the Met allele (Val/Val vs. Val/Met or Met/Met). All of the analyses were modeled after published work (Barnett et al. 2008) and conducted using Comprehensive Meta-analyses Software Version 2.0 (Biostat Inc., Englewood, NJ, USA). Specifically, the data were analysed using fixed- or random-effects approaches based on the results of the homogeneity test. If there was no evidence of heterogeneity, an assumption was made that the effect of genotype was constant across studies and whatever variance was encountered that differentiated studies were attributed to chance or random variation. If there was evidence of heterogeneity, it was assumed that the variance that differentiated the samples was due not only to chance or random variation, but also to individual study effects. Effect sizes (Cohen's d, calculated through mean differences) for each sample and each phenotype were pooled to generate summative effect sizes and 95% confidence intervals (CI); the significance of these summative effect sizes was established via a Z test. A spreadsheet with all of the data that was included in the analysis is available from the authors. In addition, sensitivity and publication bias analyses were conducted. The sensitivity analysis examines whether the inclusion of a particular study has an effect on the final results of the meta-analysis. This analysis reruns the entire analysis k times excluding one study on each pass to be able to assess the contribution of the particular study. Funnel plots were produced to examine the possibility of publication bias. Publication bias, which can skew the results of the analysis, can occur because of the nature of the literature to preferentially publish significant rather than null results.

Results

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

The results of the analyses are presented in Table 1 and in Figs. 1–5 (these figures are showing the standard difference in means and 95% CI for the difference).

Table 1. Results of the meta-analysis
  N Cohen's d effect size and 95% CITest of null (two tail)Heterogeneity
Point estimateLower limitUpper limit Z-value P-value Q-value P-value
  1. N, number of independent samples analysed.

General cognitive ability230.023−0.0400.0850.7140.47533.3540.057
Memory210.043−0.0230.1091.2800.20139.7910.005
Executive functioning170.030−0.0710.1300.5780.56332.0510.010
Visual150.019−0.0710.1090.4060.68518.7280.176
Cognitive fluency80.054−0.0240.1331.3550.17513.3400.064

Effect-size analyses

General cognitive ability

Seventeen published reports with 23 independent samples were included in the analysis (Baig et al. 2010; Cerasa et al. 2010; Chung et al. 2010; Echeverria et al. 2005; Egan et al. 2003; Erickson et al. 2008; Ho et al. 2007; Houlihan et al. 2009; Miyajima et al. 2008a; Nacmias et al. 2004; Oroszi et al. 2006; Schofield et al. 2009; Strauss et al. 2004; Tsai et al. 2004, 2008; Yu et al. 2008). The summative result showed no association between Val66Met and general cognitive ability (d = 0.023, 95% CI-0.040–0.085, Z = 0.714,P = 0.475; Table 1 and Fig. 1).

Memory

Fifteen published reports with 21 independent samples were included in the analysis (Cathomas et al. 2010; Cerasa et al. 2010; Chung et al. 2010; Echeverria et al. 2005; Egan et al. 2003; Harris et al. 2006; Ho et al. 2006, 2007; Houlihan et al. 2009; Nacmias et al. 2004; Oroszi et al. 2006; Strauss et al. 2004; Tan et al. 2005; Tsai et al. 2008; Yu et al. 2008). The meta-analysis showed no association between indicators of memory and the Val66Met polymorphism (d = 0.043, 95% CI-0.023–0.109, Z = 1.280,P = 0.201; Table 1 and Fig. 2).

Executive functioning

Eleven published reports with 17 independent samples were included in the analysis (Cerasa et al. 2010; Chung et al. 2010; Echeverria et al. 2005; Ho et al. 2006, 2007; Nacmias et al. 2004; Oroszi et al. 2006; Rybakowski et al. 2003, 2006; Tsai et al. 2008; Yu et al. 2008). No association was detected (d = 0.030, 95% CI-0.071–0.130, Z = 0.578, P = 0.563; Table 1 and Fig. 3).

Visual

Eleven published reports with 15 independent samples were analysed (Cerasa et al. 2010; Chung et al. 2010; Echeverria et al. 2005; Hansell et al. 2007; Ho et al. 2006, 2007; Nacmias et al. 2004; Oroszi et al. 2006; Strauss et al. 2004; Tsai et al. 2008; Yu et al. 2008). No association was detected (d = 0.019,95%CI-0.071–0.109,Z = 0.406, P = 0.685; Table 1 and Fig. 4).

Cognitive fluency

Seven published reports with eight independent samples were included in the analysis (Echeverria et al. 2005; Harris et al. 2006; Houlihan et al. 2009; Miyajima et al. 2008a; Nacmias et al. 2004; Tsai et al. 2008; Yu et al. 2008). Similar to the analyses presented above, no association was established (d = 0.054,95%CI-0.024–0.133,Z = 1.355,P = 0.175; Table 1 and Fig. 5).

Sensitivity and publication bias analyses

The results of the sensitivity analysis did not differ from the results presented above; the corresponding forest plots of the sensitivity analysis can be found in Figures S1–S5. Funnel plots to assess publication bias can be found in Figures S6–S10. Figures S1–S10 can be found in the supporting information.

Discussion

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

The present meta-analysis of the association of the BDNF Val66Met polymorphism with a number of indicators of cognition included 7095 individuals, both healthy and challenged by a variety of mental health conditions (Table 2). The data for this meta-analysis were drawn from 23 publications that included 31 independent samples. As far as we know, this is the first meta-analysis carried out to investigate the relationship between the BDNF Val66Met polymorphism and cognition.

Table 2. Reference numbers and sample informationThumbnail image of

Overall, this meta-analysis did not detect consistent significant genetic association between the BDNF Val66Met polymorphism and any of the investigated cognitive phenotypes. As is evident from the graphical representations of results from individual studies (Figs. 1–5), this summary is, perhaps, of no surprise, because the patterns of findings in the literature are, indeed, diverse, pointing in both directions, but regardless of the direction, indicating modest effect sizes. Of note also is that sensitivity and publication bias analyses did not change the overall pattern of results.

The conflicting findings in the literature may reflect any one of at least five different situations. First, it could be that the genetic variant (i.e. the BDNF Val66Met polymorphism) is truly not associated with the five clusters of cognitive indicators captured in this analysis. Second, it is possible that the genetic variant is associated with some intermediate phenotype (e.g. brain-based phenotype), which, in turn, is related to some but not all of the cognitive phenotypes examined here. Third, given the multitude and variability of BDNF isoforms and the diversity of its transcripts in different brain areas, it is possible that cognitive phenotypes should be grouped not by their behavioural similarities, but by similarities in the brain activation pathways that underlie these phenotypes. Fourth, it might be that this meta-analysis was hindered by reduced statistical power because of the limited sample sizes in some of the reported studies (Table 2). Fifth, the results might vary because of some hidden sources of variation that stratify the findings and were not properly reflected in our analysis. Indeed, we did register between study heterogeneity for two out of our five clusters of phenotypes. The obvious candidates for such sources of stratification are demographic characteristics such as gender, age and ethnicity as well as the type of the sample (i.e. inclusion of typical vs. disordered individuals).

It is important to note that each of these stratifying sources is present in the literature on the BDNF Val66Met polymorphism. Specifically, with regard to gender, the literature contains reports of a dimorphic association of the polymorphism and depression, in both allelic and genotypic analyses (Verhagen et al. 2010). Similarly, there is also evidence of differential age-based effects of the Val66Met polymorphism (Verhagen et al. 2010). Unfortunately, the data that substantiated the current meta-analysis did not permit the analyses of either gender or age stratifying effects on the association between the BDNF Val66Met polymorphism and cognition. Likewise, we were not able to investigate the potential impact of ethnicity, although there is a clear reason to do so. Particularly, the Met allele frequency is higher in Asians compared with Caucasians (Shimizu et al. 2004). Yet, studies used in this meta-analysis were carried out, predominantly, with individuals of European descent, as is typical in the field of genetic association studies in general (Rosenberg et al. 2010), although there are selected Chinese and Korean studies. Clearly, the potential stratifying impact of ethnic background on the putative association between the BDNF Val66Met polymorphism and cognition needs to be investigated further. Finally, as is obvious from Table 2, the samples that were utilized in these analyses include both healthy and disordered individuals. Moreover, there is a wide spectrum of disorders and diseases that are represented in these studies. Once again, there is evidence in the literature that health status can be another stratifying variable. For example, opposite effects of the Val66Met polymorphism were reported for cognitive indicators in patients with multiple sclerosis and controls (Cerasa et al. 2010). Yet, although all of these considerations are relevant, as it stands now, the field has not generated enough data to allow a close investigation of the majority of potential stratifying factors; there is just not enough information to appraise their importance at this time.

In addition, our meta-analysis raised a number of other considerations. Specifically, it is striking how many different tasks have been used in the field to assess, arguably, similar (or the same) cognitive functions. Although often reflective of the theoretical and methodological specifics of the research question at hand, this diversity also signifies a lack of convergence in the methods employed to assess similar (the same) cognitive functions. Clearly, the presence of more homogeneity in the assessment methods for similar (or the same) underlying cognitive function would have been advantageous for detecting the presence or absence of the association with the BDNF Val66Met polymorphism more unequivocally. Thus, more orchestrated approaches to phenotyping in designing genetic and allelic association studies of BDNF and other candidate genes for cognition are highly desired and are expected to be fruitful. It is our hope that this summary of the phenotypes that have been used in this context might be of assistance in making decisions on what assessments should be included for particular cognitive functions. Moreover, anatomically and physiologically, BDNF is thought to be especially prominent in the hippocampus, therefore the impact of the Val66Met polymorphisms could have been more pertinent in hippocampus-specific, fine-grained tasks (Carlson et al. 2010). Yet, the present list of phenotypes utilized in the literature on the association studies of the BDNF Val66Met polymorphism does not present an opportunity for such fine-grained analyses. Finally, as this polymorphism has been reported to be associated, although with a certain degree of inconsistency in results, with a variety of neuropsychiatric disorders (e.g. eating disorders, bipolar disorder and/or depression, anxiety and schizophrenia), it is possible that the connection between BDNF and cognition is indirect and modulated by some other factors.

Of note also is that in the analysis presented here we focused on a single, albeit functionally important, missense polymorphism of the BDNF gene. As mentioned earlier, this gene contains numerous polymorphic sites and the frequencies of ancestral alleles at many of these sites are highly variable around the world; there is also evidence of a substantial amount of linkage disequilibrium between the polymorphic sites of BDNF and of positive selection at the BDNF locus, at least in pedigrees of European origin (Petryshen et al. 2010). All these phenomena underline the importance of carrying out not only single polymorphism, but also haplotype analyses to show associations not only with particular alleles, but also with combinations of alleles (i.e. haplotypes) and disorders (Borroni et al. 2009; Suchanek et al. 2011; You et al. 2010), as well as cognitive and intermediate (Juckel et al. 2010) phenotypes. As the nature of genotyping changes and single-polymorphism studies are being replaced by targeted dense genotyping of a genetic region of interest or whole-genome genotyping, more research that investigates broad patterns of association between variations in BDNF and complex behaviour can be carried out. In addition, it is possible that, for the effect to be consistently detectable in numerous samples, it has to be coupled with effects of other genes in the BDNF pathway, either additive or interactive (Lin et al. 2009; Miyajima et al. 2008b; Ribases et al. 2008). Clearly, the BDNF gene is complex structurally, functionally and evolutionarily, and this complexity should be taken into consideration both at the design and result interpretation stages of association studies of BDNF, as they can influence strongly the likelihood of the detection of the susceptibility alleles for specific phenotypes, whether holistic (i.e. cognitive function or disorder) or intermediate (i.e. brain-based) and the appraisal of the magnitude of their effects.

In conclusion, although the Val66Met BDNF polymorphism remains an object of curiosity and investigative activities of researchers interested in the aetiology of individual differences in cognition, this meta-analysis did not show any evidence of association between this polymorphism and the five studied indicators of cognition. This is, of course, disappointing, as both the gene and the polymorphism are, theoretically, excellent candidates for association with cognition. It is also disappointing because the literature on the connection between BDNF and complex human behaviour spans a large spectrum of phenotypes and samples, contains numerous interesting insights, and yet is rife with contradictory findings. Nevertheless, although the results of this meta-analysis are not encouraging, the factors discussed above, uniquely and collectively, can explain these results. Also, as the evidence accumulates and the collective understanding of the connection between genes and cognition grows, such meta-analyses as the one presented here might appear to be helpful, providing a summative, although perhaps not decisive, given their own limitations, overview of the field.

References

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information
  • Baig, B.J., Whalley, H.C., Hall, J., McIntosh, A.M., Job, D.E., Cunningham-Owens, D.G., Johnstone, E.C. & Lawrie, S.M. (2010) Functional magnetic resonance imaging of BDNF val66met polymorphism in unmedicated subjects at high genetic risk of schizophrenia performing a verbal memory task. Psychiat Res-Neuroim 183, 195201.
  • Barnett, J.H., Scoriels, L. & Munafò, M.R. (2008) Meta-analysis of the cognitive effects of the catechol-o-methyltransferase gene Val158/108Met polymorphism. Biol Psychiatry 64, 137144.
  • Beste, C., Kolev, V., Yordanova, J., Domschke, K., Falkenstein, M., Baune, B.T. & Konrad, C. (2010) The role of the BDNF Val66Met polymorphism for the synchronization of error-specific neural networks. J Neurosci 30, 1072710733.
  • Bishop, J.F., Mueller, G.P. & Mouradian, M.M. (1994) Alternate 5’ exons in the rat brain-derived neurotrophic factor gene: differential patterns of expression across brain regions. Brain Res Mol Brain Res 26, 225232.
  • Borroni, B., Grassi, M., Archetti, S., Costanzi, C., Bianchi, M., Caimi, L., Caltagirone, C., Di Luca, M. & Padovani, A. (2009) BDNF genetic variations increase the risk of Alzheimer's disease-related depression. J Alzheimer's Dis 18, 867875.
  • Carlson, E.S., Fretham, S.J., Unger, E., O’Connor, M., Petryk, A., Schallert, T., Rao, R., Tkac, I. & Georgieff, M.K. (2010) Hippocampus specific iron deficiency alters competition and cooperation between developing memory systems. J Neurodev Disord 2, 133143.
  • Carvalho, A.L., Caldeira, M.V., Santos, S.D. & Duarte, C.B. (2008) Role of the brain-derived neurotrophic factor at glutamatergic synapses. Br J Pharmacol 153, S310S324.
  • Cathomas, F., Vogler, C., Euler-Sigmund, J.C., de Quervain, D.J.-F. & Papassoitripolous, A. (2010) Fine-mapping of the brain-derived neurotrophic factor (BDNF) gene supports an association of the Val66Met polymorphism with episodic memory. Int J Neuropsychopharmacol 13, 975980.
  • Cerasa, A., Tongiorgi, E., Fera, F., Gioia, M.C., Valentino, P., Liguori, M., Manna, I., Zito, G., Passamonti, L., Nisticò, R. & Quattrone, A. (2010) The effects of BDNF Val66Met polymorphism on brain function in controls and patients with multiple sclerosis: an imaging genetic study. Behav Brain Res 207, 377386.
  • Chao, M.V., Rajagopal, R. & Lee, F.S. (2006) Neurotrophin signalling in health and disease. Clin Sci 110, 167173.
  • Chen, Z.Y., Patel, P.D., Sant, G., Meng, C.X., Teng, K.K., Hempstead, B.L. & Lee, F.S. (2004) Variant brain-derived neurotrophic factor (BDNF) (Met66) alters the intracellular trafficking and activity-dependent secretion of wild-type BDNF in neurosecretory cells and cortical neurons. J Neurosci 24, 44014411.
  • Chung, S., Chung, H.Y., Jung, J., Chang, J.K. & Hong, J.P. (2010) Association among aggressiveness, neurocognitive function, and the Val66Met polymorphism of brain-derived neurotrophic factor gene in male schizophrenic patients. Compr Psychiatry 51, 367372.
  • Cowley, S., Paterson, H., Kemp, P. & Marshall, C.J. (1994) Activation of MAP kinase kinase is necessary and sufficient for PC12 differentiation and for transformation of NIH 3T3 cells. Cell 77, 841852.
  • Echeverria, D., Woods, J.S., Heyer, N.J., Rohlman, D.S., Farin, F.M., Bittner, J.A.C., Li, T. & Garabedian, C. (2005) Chronic low-level mercury exposure, BDNF polymorphism, and associations with cognitive and motor function. Neurotoxicol Teratol 27, 781796.
  • Egan, M.F., Kojima, M., Callicott, J.H., Goldberg, T.E., Kolachana, B.S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., Dean, M., Lu, B. & Weinberger, D.R. (2003) The BDNF Val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 112, 257269.
  • Erickson, K.I., Kim, J.S., Suever, B.L., Voss, M.W., Francis, B.M., & Kramer, A.F. (2008) Genetic contributions to age-related decline in executive function: a 10-year longitudinal study of COMT and BDNF polymorphisms. Frontiers in Human Neuroscience 2.
  • Frielingsdorf, H., Bath, K.G., Soliman, F., DiFede, J., Casey, B.J. & Lee, F.S. (2010) Variant brain-derived neurotrophic factor Val66Met endophenotypes: implications for posttraumatic stress disorder. Ann N Y Acad Sci 1208, 150157.
  • Fuchikami, M., Morinobu, S., Kurata, A., Yamamoto, S. & Yamawaki, S. (2009) Single immobilization stress differentially alters the expression profile of transcripts of the brain-derived neurotrophic factor (BDNF) gene and histone acetylation at its promoters in the rat hippocampus. Int J Neuropharmacol 12, 7382.
  • Gong, P., Zheng, A., Chen, D., Ge, W., Lv, C., Zhang, K., Gao, X. & Zhang, F. (2009) Effect of BDNF Val66Met polymorphism on digital working memory and spatial localization in a healthy Chinese Han population. J Mol Neurosci 38, 250256.
  • Greenberg, M.E., Xu, B., Lu, B. & Hempstead, B.L. (2009) New insights in the biology of BDNF synthesis and release: implications in CNS function. J Neurosci 29, 1276412767.
  • Guillin, O., Diaz, J., Carroll, P., Griffon, N., Schwartz, J.C. & Sokoloff, P. (2001) BDNF controls dopamine D3 receptor expression and triggers behavioural sensitization. Nature 411, 8689.
  • Hansell, N.K., James, M.R., Duffy, D.L., Birley, A.J., Luciano, M., Geffen, G.M., Wright, M.J., Montgomery, G.W. & Martin, N.G. (2007) Effect of the BDNF V166M polymorphism on working memory in healthy adolescents. Genes Brain Behav 6, 260268.
  • Harris, S.E., Fox, H., Wright, A.F., Hayward, C., Starr, J.M., Whalley, L.J. & Deary, I.J. (2006) The brain-derived neurotrophic factor Val66Met polymorphism is associated with age-related change in reasoning skills. Mol Psychiatry 11, 505513.
  • Ho, B.-C., Andreasen, N.C., Dawson, J.D. & Wassink, T.H. (2007) Association between brain-derived neurotrophic factor Val66Met gene polymorphism and progressive brain volume changes in schizophrenia. Am J Psychiatry 164, 18901899.
  • Ho, B.-C., Milev, P., O’Leary, D.S., Librant, A., Andreasen, N.C. & Wassink, T.H. (2006) Cognitive and magnetic resonance imaging brain morphometric correlates of brain-derived neurotrophic factor Val66Met gene polymorphism in patients with schizophrenia and healthy volunteers. Arch Gen Psychiatry 63, 731740.
  • Houlihan, L.M., Harris, S.E., Luciano, M., Gow, A.J., Starr, J.M., Visscher, P.M. & Deary, I.J. (2009) Replication study of candidate genes for cognitive abilities: the Lothian Birth Cohort 1936. Genes Brain Behav 8, 238247.
  • Juckel, G., Schumacher, C., Giegling, I., Assion, H.J., Mavrogiorgou, P., Pogarell, O., Mulert, C., Hegerl, U., Norra, C. & Rujescu, D. (2010) Serotonergic functioning as measured by the loudness dependence of auditory evoked potentials is related to a haplotype in the brain-derived neurotrophic factor (BDNF) gene. J Psychiatr Res 44, 541546.
  • Karnik, M.S., Wang, L., Barch, D.M., Morris, J.C. & Csernansky, J.G. (2010) BDNF polymorphism rs6265 and hippocampal structure and memory performance in healthy control subjects. Psychiatry Res 178, 425429.
  • Lang, U.E., Hellweg, R., Kalus, P., Bajbouj, M., Lenzen, K.P., Sander, T., Kunz, D. & Gallinat, J. (2005) Association of a functional BDNF polymorphism and anxiety-related personality traits. Psychopharmacol 180, 9599.
  • Lau, A.G., Irier, H.A., Gu, J., Tian, D., Ku, L., Liu, G., Xia, M., Fritsch, B., Zheng, J.Q., Dingledine, R., Xu, B., Lu, B. & Feng, Y. (2010) Distinct 3’UTRs differentially regulate activity-dependent translation of brain-derived neurotrophic factor (BDNF). Proc Natl Acad Sci U S A 107, 1594515950.
  • Lee, R., Kermani, P., Teng, K.K. & Hempstead, B.L. (2001) Regulation of cell survival by secreted proneurotrophins. Science 294, 19451948.
  • Lin, E., Hong, C.J., Hwang, J.P., Liou, Y.J., Yang, C.H., Cheng, D. & Tsai, S.J. (2009) Gene-gene interactions of the brain-derived neurotrophic-factor and neurotrophic tyrosine kinase receptor 2 genes in geriatric depression. Rejuvenation Res 12, 387393.
  • Marmigere, F., Givalois, L., Rage, F., Arancibia, S. & Tapia-Arancibia, L. (2003) Rapid induction of BDNF expression in the hippocampus during immobilization stress challenge in adult rats. Hippocampus 13, 646655.
  • Matsuo, K., Walss-Bass, C., Nery, F.G. et al. (2009) Neuronal correlates of brain-derived neurotrophic factor Val66Met polymorphism and morphometric abnormalities in bipolar disorder. Neuropsychopharmacology 34, 19041913.
  • Mazzucchelli, C., Vantaggiato, C., Ciamei, A. et al . (2002) Knockout of ERK1 MAP kinase enhances synaptic plasticity in the striatum and facilitates striatal-mediated learning and memory. Neuron 34, 807820.
  • Miyajima, F., Ollier, W., Mayes, A., Jackson, A., Thacker, N., Rabbitt, P., Pendleton, N., Horan, M. & Payton, A. (2008a) Brain-derived neurotrophic factor polymorphism Val66Met influences cognitive abilities in the elderly. Genes Brain Behav 7, 411417.
  • Miyajima, F., Quinn, J.P., Horan, M., Pickles, A., Ollier, W.E., Pendleton, N. & Payton, A. (2008b) Additive effect of BDNF and REST polymorphisms is associated with improved general cognitive ability. Genes Brain Behav 7, 714719.
  • Mossner, R., Daniel, S., Albert, D., Heils, A., Okladnova, O., Schmitt, A. & Lesch, K.P. (2000) Serotonin transporter function is modulated by brain-derived neurotrophic factor (BDNF) but not nerve growth factor (NGF). Neurochem Int 36, 197202.
  • Nacmias, B., Piccini, C., Bagnoli, S., Tedde, A., Cellini, E., Bracco, L. & Sorbi, S. (2004) Brain-derived neurotrophic factor, apolipoprotein E genetic variants and cognitive performance in Alzheimer's disease. Neurosci Lett 367, 379383.
  • Oroszi, G., Lapteva, L., Davis, E., Yarboro, C.H., Weickert, T., Roebuck-Spencer, T., Bleiberg, J., Rosenstein, D., Pao, M., Lipsky, P.E., Goldman, D., Lipsky, R.H. & Illei, G.G. (2006) The Met66 allele of the functional Val66Met polymorphism in the brain-derived neurotrophic factor gene confers protection against neurocognitive dysfunction in systemic lupus erythematosus. Ann Rheum Dis 65, 13301335.
  • Petryshen, T.L., Sabeti, P.C., Aldinger, K.A., Fry, B., Fan, J.B., Schaffner, S.F., Waggoner, S.G., Tahl, A.R. & Sklar, P. (2010) Population genetic study of the brain-derived neurotrophic factor (BDNF) gene. Mol Psychiatry 15, 810815.
  • Pruunsild, P., Kazantseva, A., Aid, T., Palm, K. & Timmusk, T. (2007) Dissecting the human BDNF locus: bidirectional transcription, complex splicing, and multiple promoters. Genomics 90, 397406.
  • Ribases, M., Hervas, A., Ramos-Quiroga, J.A., Bosch, R., Bielsa, A., Gastaminza, X., Fernandez-Anguiano, M., Nogueira, M., Gomez-Barros, N., Valero, S., Gratacos, M., Estivill, X., Casas, M., Cormand, B. & Bayes, M. (2008) Association study of 10 genes encoding neurotrophic factors and their receptors in adult and child attention-deficit/hyperactivity disorder. Biol Psychiatry 63, 935945.
  • Rosenberg, N.A., Huang, L., Jewett, E.M., Szpiech, Z.A., Jankovic, I. & Boehnke, M. (2010) Genome-wide association studies in diverse populations. Nat Rev Genet 11, 356366.
  • Rosenblum, K., Futter, M., Voss, K., Erent, M., Skehel, P.A., French, P., Obosi, L., Jones, M.W. & Bliss, T.V. (2002) The role of extracellular regulated kinases I/II in late-phase long-termpotentiation. J Neurosci 22, 54325441.
  • Roux, P.P. & Barker, P.A. (2002) Neurotrophin signaling through the p75 neurotrophin receptor. Prog Neurobiol 67, 203233.
  • Rybakowski, J.K., Borkowska, A., Czerski, P.M., Skibinska, M. & Hauser, J. (2003) Polymorphism of the brain-derived neurotrophic factor gene and performance on a cognitive prefrontal test in bipolar patients. Bipolar Disord 5, 468472.
  • Rybakowski, J.K., Borkowska, A., Skibinska, M. & Hauser, J. (2006) Illness-specific association of val66met BDNF polymorphism with performance on Wisconsin Card Sorting Test in bipolar mood disorder. Mol Psychiatry 11, 122124.
  • Schofield, P.R., Williams, L.M., Paul, R.H., Gatt, J.M., Brown, K., Luty, A., Cooper, N., Grieve, S., Dobson-Stone, C., Morris, C., Kuan, S.A. & Gordon, E. (2009) Disturbances in selective information processing associated with the BDNF Val66Met polymorphism: evidence from cognition, the P300 and fronto-hippocampal systems. Biol Psychol 80, 176188.
  • Shimizu, E., Hashimoto, K. & Iyo, M. (2004) Ethnic difference of the BDNF 196G/A [val66met] polymorphism frequencies: the possibility to explain ethnic mental traits. Am J Med Genet B Neuropsychiatr Genet 126B, 122123.
  • Soliman, F., Glatt, C.E., Bath, K.G., Levita, L., Jones, R.M., Pattwell, S.S., Jing, D., Tottenham, N., Amso, D., Somerville, L.H., Voss, H.U., Glover, G., Ballon, D.J., Liston, C., Teslovich, T., Van Kempen, T., Lee, F.S. & Casey, B.J. (2010) A genetic variant BDNF polymorphism alters extinction learning in both mouse and human. Science 327, 863866.
  • Strauss, J., Barr, C., George, C., Ryan, C., King, N., Shaikh, S., Kovacs, M. & Kennedy, J. (2004) BDNF and COMT polymorphisms. Neuromolecular Med 5, 181192.
  • Suchanek, R., Owczarek, A., Kowalczyk, M., Kucia, K. & Kowalski, J. (2011) Association between C-281A and val66met functional polymorphisms of BDNF gene and risk of recurrent major depressive disorder in Polish population. J Mol Neurosci 43, 524530.
  • Tan, Y.L., Zhou, D.F., Cao, L.Y., Zou, Y.Z., Wu, G.Y. & Zhang, X.Y. (2005) Effect of the BDNF Val66Met genotype on episodic memory in schizophrenia. Schizophr Res 77, 355356.
  • Teng, K.K., Lee, R., Wright, S., Tevar, S., Almeida, R.D., Kermani, P., Torkin, R., Chen, Z.Y., Lee, F.S., Kraemer, R.T., Nykjaer, A. & Hempstead, B.L. (2005) ProBDNF induces neuronal apoptosis via activation of a receptor complex of p75NTR and sortilin. J Neurosci 25, 54555463.
  • Timmusk, T., Palm, K., Metsis, M., Reintam, T., Paalme, V., Saarma, M. & Persson, H. (1993) Multiple promoters direct tissue-specific expression of the rat BDNF gene. Neuron 10, 475489.
  • Tognoli, C., Rossi, F., Di Cola, F., Baj, G., Tongiorgi, E., Terova, G., Saroglia, M., Bernardini, G. & Gornati, R. (2010) Acute stress alters transcript expression pattern and reduces processing of proBDNF to mature BDNF in Dicentrarchus labrax. BMC Neurosci 11, 4.
  • Tsai, S.-J., Hong, C.-J., Yu, Y.W.Y. & Chen, T.-J. (2004) Association study of a Brain-Derived Neurotrophic Factor (BDNF) Val66Met polymorphism and personality trait and intelligence in healthy young females. Neuropsychobiology 49, 1316.
  • Tsai, S.-J., Gau, Y.-T.A., Liu, M.-E., Hsieh, C.-H., Liou, Y.-J. & Hong, C.-J. (2008) Association study of brain-derived neurotrophic factor and apolipoprotein E polymorphisms and cognitive function in aged males without dementia. Neurosci Lett 433, 158162.
  • Verhagen, M., van der Meij, A., van Deurzen, P.A.M., Janzing, J.G.E., Arias-Vasquez, A., Buitelaar, J.K. & Franke, B. (2008) Meta-analysis of the BDNF Val66Met polymorphism in major depressive disorder: effects of gender and ethnicity. Mol Psychiatry 15, 260271.
  • Verhagen, M., van der Meij, A., van Deurzen, P.A.M., Janzing, J.G.E., Arias-Vasquez, A., Buitelaar, J.K. & Franke, B. (2010) Meta-analysis of the BDNF Val66Met polymorphism in major depressive disorder: effects of gender and ethnicity. Mol Psychiatry 15, 260271.
  • You, J., Yuan, Y., Zhang, Z., Zhang, X., Li, H. & Qian, Y. (2010) A preliminary association study between brain-derived neurotrophic factor (BDNF) haplotype and late-onset depression in mainland Chinese. J Affect Disord 120, 165169.
  • Yu, H., Zhang, Z., Shi, Y., Bai, F., Xie, C., Qian, Y., Yuan, Y. & Deng, L. (2008) Association study of the decreased serum BDNF concentrations in amnestic mild cognitive impairment and the Val66Met polymorphism in Chinese Han. J Clin Psychiatry 69, 11041111.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

The preparation of this article was supported by funds from the US National Institutes of Health (NIH; awards DC007665, PI Grigorenko, and HD052120, PI Wagner). Grantees undertaking such projects are encouraged to freely express their professional judgement. This article, therefore, does not necessarily represent the position or policies of the NIH and no official endorsement should be inferred. We are thankful to the researchers who provided their data for these analyses. We are also thankful to Ms Mei Tan for her editorial assistance and to Ms Magge Gagliardi for her help with figures.

Supporting Information

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Supporting Information

Additional Supporting Information may be found in the online version of this article:

Figure S1: Sensitivity analysis: cognitive abilities.

Note: Val: Val/Val homozygotes, Met: Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of the study in the analysis; lines represent the confidence interval for the finding of the study; arrow represents the findings that are beyond the scale set for the figure; and diamond represents the overall effect of the meta-analysis.

Unspecified IQ test: 1a, 1b, 1c (Egan et al. 2003)

The Wechsler Adult Intelligence Scale (WAIS): 2 (Tsai et al. 2004), 3 (Oroszi et al. 2006), 4a, 4b (Ho et al. 2006), 5a, 5b (Cerasa et al. 2010), 6a, 6b (Chung et al. 2010)

The Wechsler Intelligence Scale for Children (WISC): 7 verbal IQ (Strauss et al. 2004)

Test of nonverbal intelligence: 8 (Echeverria et al. 2005)

Kaufman brief intelligence test: 9 (Erickson et al. 2008)

Heim intelligence test: 10 (Miyajima et al. 2008)

Estimated IQ (by spot word test): 11 (Schofield et al. 2009)

Cognitive abilities screening instrument, Chinese version: 12 (Tsai et al. 2008)

g factor: 13 (Houlihan et al. 2009)

Mini mental state examination: 14 (Nacmias et al. 2004), 15a, 15b (Yu et al. 2008)

Problem solving: 16 (Ho et al. 2007)

National adult reading test: 17 (Baig et al. 2010).

Figure S2: Working memory: 1a, 1b, 1c (Egan et al. 2003); Wechsler Memory Scale: 2 (Strauss et al. 2004), 3 (Tan et al. 2005), 4 (Harris et al. 2006), 5a, 5b (Ho et al. 2006); digit span: 6 (Nacmias et al. 2004), 7 (Echeverria et al. 2005), 8a, 8b (Yu et al. 2008), 9 (Houlihan et al. 2009), 10a, 10b (Cerasa et al. 2010); verbal memory: 11 (Ho et al. 2007); delayed free recall: 12 (Oroszi et al. 2006); Rey-Kim assessment (a Korean memory assessment): 13a, 13b (Chung et al. 2010); and long delay: 14 (Tsai et al. 2008), 15 (Cathomas et al. 2010).

Figure S3: Sensitivity analysis: executive functioning.

Note: Val: Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of the study in the analysis; lines represent the confidence interval for the finding of the study; arrow represents findings that are beyond the scale set for the figure; and diamond represents the overall effect of the meta-analysis.

Wisconsin card storing task: 1 (Rybakowski et al. 2003), 2a, 2b, 2c (Rybakowski et al. 2006), 3a, 3b (Cerasa et al. 2010), 4a, 4b (Chung et al. 2010)

Trails: 5 (Echeverria et al. 2005), 6 (Oroszi et al. 2006), 7a, 7b (Yu et al. 2008)

Attention: 8a, 8b (Ho et al. 2006), 9 (Ho et al. 2007), 10 (Tsai et al. 2008)

Information–memory–concentration test: 11 (Nacmias et al. 2004).

Figure S4: Sensitivity analysis: visual phenotype.

Note: Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of the study in the analysis; lines represent the confidence interval for the finding of the study; arrow represents the findings that are beyond the scale set for the figure; and diamond represents the overall effect of the meta-analysis.

Rey–Osterrieth complex figure test: 1 (Strauss et al. 2004), 2 (Oroszi et al. 2006), 3a, 3b (Ho et al. 2006), 4 (Ho et al. 2007), 5a, 5b (Yu et al. 2008), 6a, 6b (Cerasa et al. 2010), 7a, 7b (Chung et al. 2010)

Copying drawing: 8 (Nacmias et al. 2004)

Visual reproduction/construction: 9 (Echeverria et al. 2005), 10 (Tsai et al. 2008)

Trials correct (i.e. correct identification of a visual target in a computerized visuospatial delayed-response task): 11(Hansell et al. 2007).

Figure S5: Sensitivity analysis cognitive fluency.

Note: Val, Val/Val homozygotes; Met, Met allele carriers; boxes represent the effect estimates, the size of the boxes represents the weighting of the study in the analysis; lines represent the confidence interval for the finding of the study; arrow represents the findings that are beyond the scale set for the figure; and diamond represents the overall effect of the meta-analysis.

Letter fluency: 1 (Nacmias et al. 2004), 2 (Miyajima et al. 2008)

Vocabulary/verbal fluency: 3 (Echeverria et al. 2005), 4 (Harris et al. 2006), 5 (Houlihan et al. 2009)

List-generating fluency: 6 (Tsai et al. 2008)

Category fluency: 7a, 7b (Yu et al. 2008).

Figure S6: Cognitive ability publication bias analysis.

Figure S7: Memory publication bias analysis.

Figure S8: Executive function publication bias analysis.

Figure S9: Visual publication bias analysis.

Figure S10: Fluency publication bias analysis.

As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

FilenameFormatSizeDescription
GBB_738_sm_fs1_fs10.doc770KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.