New Results

Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank

LM Reus, X Shen, J Gibson, E Wigmore, L Ligthart, MJ Adams, G Davies, SR Cox, SP Hagenaars, ME Bastin, IJ Deary, HC Whalley, AM McIntosh
doi: https://doi.org/10.1101/080283

Abstract

Major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BP) are common, disabling and heritable psychiatric diseases with a complex overlapping polygenic architecture. Individuals with these disorders, as well as their unaffected relatives, show widespread structural differences in corticostriatal and limbic networks. Structural variation in many of these brain regions is also heritable and polygenic but whether their genetic architecture overlaps with major psychiatric disorders is unknown. We sought to address this issue by examining the impact of polygenic risk of MDD, SCZ, and BP on subcortical brain volumes and white matter (WM) microstructure in a large single sample of neuroimaging data; the UK Biobank Imaging study. The first release of UK Biobank imaging data compromised participants with overlapping genetic data and subcortical volumes (N = 978) and WM measures (N = 816). Our, findings however, indicated no statistically significant associations between either subcortical volumes or WM microstructure, and polygenic risk for MDD, SCZ or BP. In the current study, we found little or no evidence for genetic overlap between major psychiatric disorders and structural brain measures. These findings suggest that subcortical brain volumes and WM microstructure may not be closely linked to the genetic mechanisms of major psychiatric disorders.

Introduction

Major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BP) are major psychiatric disorders affecting between 1% and 13% of the general population1,2. Twin and family studies find a strong genetic contribution to all three of these disorders3,4 and genome-wide association studies (GWAS) suggest that these effects are conferred by the cumulative effect of many loci, each of small effect5–7. Whilst progress in identifying the specific risk-conferring loci in SCZ has proved highly productive5, progress in BD and MDD has been slower6,7. Several strategies may accelerate the discovery of risk-loci, including the use of quantitative traits such as brain structure and connectivity.

Brain structure and connectivity measures have been shown to be heritable quantitative traits, with high heritabilities reported for both subcortical volumes (44-88%)8 and white matter (WM) integrity (53-90%)9. Furthermore, several genetic variants that influence brain structure have been identified10,11. Subcortical brain volume and WM microstructure abnormalities in corticostriatal and limbic networks have also been shown to distinguish individuals with SCZ, BD and MDD from controls in several studies, albeit with some inconsistencies12–17. Differences between patients and controls in global measures of WM microstructure, diffusion tensor imaging (DTI) biomarkers fractional anisotropy (FA) and mean diffusivity (MD), have been more consistently reported in these disorders18–23, and could suggest wide-spread WM integrity reductions.

Together, these volumetric and WM brain differences potentially describe a network of abnormality associated with major psychiatric illness. This network may have an important mechanistic role in the development of these disorders. This possibility is supported by several studies that show an association between brain structure and genetic liability for psychiatric disorders, though due to the cost of imaging these samples are typically an order of magnitude smaller in size than the GWAS that preceded them. Notably, Van Scheltinga et al., (2013)24 found higher polygenic risk for SCZ to be modestly (R2 = 0.048) associated with smaller total brain volume, while Caseras et al., (2015)25 reported a negative association between polygenic risk scores (PGRS) for SCZ and BP, and globus pallidus and amygdala grey matter (GM) volumes. Similar results have been found for the association between less optimal WM microstructure and greater genetic liability for SCZ24,26,27.

These associations have not been consistently reported; specifically, several studies have found no association between subcortical volumes and polygenic risk for MDD and SCZ28–30. However, these studies have used differing methodologies (ie., multi-scanner study30) with relatively small sample sizes (N28/N29 = 438/122), which may contribute to inconsistencies in findings.

The current study sought to investigate the genetic relationship between major mental disorders and structural brain measures in a single large quality-controlled dataset using the same scanner and same analysis pipelines. We used newly acquired data from the first release of the UK Biobank Imaging study to examine whether structural imaging measures, including subcortical volumes (N=978) and WM microstructure measures (N=816), were associated with genetic risk of MDD, SCZ, and BP32 (http://www.ukbiobank.ac.uk). We hypothesized that higher polygenetic risk for these disorders would be associated with decreased volumetric and WM integrity measures in specifically corticostriatal and limbic networks.

Methods

Study population

The first release of UK Biobank imaging data consisted of 4,446 subjects (Nfemales/Nmales = 2,342/2,104; mean age ± s.d. = 55.52 ± 7.62 years) with quality-checked volumetric and DTI data for the analysis (conducted by UK Biobank, Brain Imaging Documentation V1.1, http://www.ukbiobank.ac.uk)33. In the current study, subjects were excluded if they did not provide genetic data, or if they were known to have participated in studies from the Psychiatric Genomics Consortium (PGC) GWAS dataset as this was used to construct the polygenic risk scores (PGRS). In total, the volumetric analysis included 978 subjects (Nfemales/Nmales = 499/479; mean age ± s.d. = 55.32 ± 7.38 years) and the DTI analysis included 816 subjects (Nfemales/Nmales = 413/403; mean age ± s.d. = 55.49 ± 7.26 years). In addition, a sensitivity analysis was performed where we imposed internal imaging quality checks excluding individuals whose subcortical volume or FA values were greater than three standard deviations above or below the mean for that measure. The sensitivity analysis, further referred as the sample excluding outliers, included 892 (Nfemales/Nmales = 477/415; mean age ± s.d. = 55.29 ± 7.31 years) and 733 (Nfemales/Nmales = 373/360; mean age ± s.d. = 55.31 ± 7.22 years) subjects for the volumetric and DTI analysis respectively.

This study has been approved by the National Health Service (NHS) Research Ethics Service (approval letter dated 17th June 2011, reference: 11/NW/0382), and by the UK Biobank Access Committee (Project #4844). Written informed consent was obtained from each subject.

Genotyping and derivation of polygenic risk scores

Procedures for DNA collection and genotyping in UK Biobank have been described previously by Hagenaars et al., (2016)34. Subjects were excluded from the PGRS analysis if they had a non-British ancestry, gender mismatch, relatedness (r>0.044), and genotype missingness bigger than 2%.

PGRS for the three traits of interest (i.e., MDD, SCZ, BP) were created using PRSice35. For each subject PGRS were calculated by adding the sum of each allele weighted by the strength of its association with the trait of interest. The strength of this association has been calculated previously by the PGC5–7,36. Prior to calculating the PGRS, single nucleotide polymorphisms (SNPs) were excluded if they had a minor allele frequency less than 1%, deviated significantly from Hardy-Weinberg equilibrium (p < 1x10-6) in the total sample of founder individuals, or had a call rate of less than 99%. The remaining SNPs were used to calculate 15 multidimensional scaling (MDS) ancestry components (to account for population structure). For the main results, the SNP inclusion threshold was set as p ≤ 0.5, as Purcell et al., (2009)37 and studies following this work, have shown that this threshold is generally the most predictive of case-control status in subsequent studies5–7,36. Using the p ≤ 0.5 SNP inclusion threshold, 631,763, 5,422,836 and 1,283,324 SNPs (after LD pruning) were included for the calculation of MDD-PGRS, SCZ-PGRS and BP-PGRS, respectively. Results generated by a SNP inclusion threshold of p ≤ 0.01, p ≤ 0.05, p ≤ 0.1 and p ≤ 1.0 are reported in supplementary materials.

Image acquisition and Neuroimaging data pre-processing

Procedures for image acquisition and pre-processing are available on the UK Biobank website (http://www.ukbiobank.ac.uk/), and have been documented previously38. More detailed information is reported in the supplementary materials.

Statistical analysis

All analyses were performed using R (version 3.2.3) in the Linux environment (R Development Core Team, 2010). To examine whether higher PGRS were associated with globally poor WM microstructure, general components for FA (gFA) and MD (gMD) across the 27 WM tracts were calculated38. Latent measures, explaining a portion of variance WM structure, were calculated for both FA and MD by extracting the scores on the first unrotated components, using a principal component analysis (PCA) on the 27 WM tracts. The first components accounted for a mean of 44.82% and 41.73% of total variance in FA and, 47.45% and 45.35% in MD, in the sample including and excluding outliers respectively (loadings of tracts on the first latent component > 0.15). To examine whether higher PGRS were selectively associated with poor WM structure in a certain classification, this PCA was also performed separately for groups of different categories of WM tracts, including association (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, uncinate fasciculus, cingulum), projection (forceps minor, forceps major, corticospinal tracts, acoustic radiation, medial lemniscus, middle cerebellar peduncle) and thalamic radiation fibers, separately38 (sample including outliers: variance explained = 54.52%, 38.68% and 61.92% respectively for each subset, and loading of first component > 0.33; sample excluding outliers: variance explained = 60.05%, 32.05% and 72.05% respectively for each subset and loading of first component > 0.27).

For bilateral brain regions, associations between the three PGRSs and each imaging measure were examined using a repeated measure linear mixed-effects analysis, modelling hemisphere as a random factor (where the PGRS * hemisphere interaction itself was found to be non-significant). For unilateral structures (e.g. total GM volume, forceps major, forceps minor and middle cerebellar peduncle), and those where there was a significant interaction between PGRS and hemisphere, associations were analysed as single structures without repeated measurements and without hemisphere as a separate term in the model.

For all models, additional fixed effects included age, age2, gender, genotype batch, genotype array and 15 MDS components. Intracranial volume, calculated as the sum of WM, GM and ventricular CSF, was also modelled as a fixed effect in the analysis of subcortical volumes in order to adjust for differences in overall brain size. PGRS and imaging measures were scaled to zero mean and unitary standard deviation. Standardised Beta values are reported throughout results. Results were corrected for multiple comparisons using a false discovery rate (FDR) correction39. Nagelkerke's R2 was estimated to assess the proportion of variation explained by PGRS.

Post-hoc positive control analyses, with age as an independent variable and each of the brain measures as dependent variable, were performed in order to test the sensitivity of UK Biobank imaging, by verifying that we could replicate the widely reported negative associations between many structural brain measures and age (including those reported previously in an overlapping sample in UK Biobank38). Fixed effects included gender for all structures, and side of hemisphere for lateralized structures only.

Results

PGRS and total brain and subcortical volumes

No significant associations were observed between PGRS (MDD, SCZ, or BP) at the threshold of p ≤ 0.5 and total GM, WM or CSF volume, in samples including or excluding outliers (Table 1). Furthermore, no significant associations were found between any of the three sets of PGRS and total GM, WM and CSF volume for any of the other PGRS p-value thresholds (all β < 0.054; all puncorrected and p FDR > 0.05) (Table S1).

View this table:
Table 1. Results - PGRS and total brain and subcortical volumes

Association of PGRS (MDD, SCZ or BP) at p ≤ 0.5 with total grey matter, white matter, cerebrospinal fluid, and subcortical volumes, in sample including and excluding outliers.

A modest negative association was observed between thalamus volumes and the polygenic risk for SCZ at the threshold of p ≤ 0.05, p ≤ 0.1, p ≤ 0.5 and p ≤ 1.0 (β range = −0.043 – −0.039; puncorrected = 0.052, puncorrected = 0.030, puncorrected = 0.054, puncorrected = 0.048 for all PGRS thresholds respectively) (Table 1 and S3). However, following FDR-correction no significant associations were found between PGRS (MDD, SCZ, or BP) at the threshold of p ≤ 0.5 and all subcortical volumes, before and after exclusion of outliers (Table 1). These results were unchanged at the other p-value thresholds (Table S2, S3, S4). For subcortical volumetric measures, no significant PGRS * hemisphere interactions were found; therefore, all analyses of bilateral structures were conducted using a repeated measures design with hemisphere as a repeated factor.

PGRS and diffusion measures

Associations between PGRS (MDD, SCZ, BP) and measures of general WM microstructure (gFA and gMD) were not significant (gFA β range = −0.048 – 0.017; p range = 0.216 – 0.771, gMD β range = −0.047 – 0.031; p range = 0.181 – 0.985) (Table 2 and 3). Moreover, when gFA and gMD were calculated separately for projection, association, and thalamic radiation fibres, there remained no signification association between these measures and PGRS for MDD, SCZ and BP at p ≤ 0.5 threshold (Table 2 and 3). These findings were similar at other p-value thresholds (Table S5 and S6).

View this table:
Table 2. Results - PGRS and diffusion measures

Association of PGRS (MDD, SCZ or BP) at p ≤ 0.5 with gFA and gFA for association, projection and thalamic WM fibers.

View this table:
Table 3.

Association of PGRS (MDD, SCZ or BP) at p ≤ 0.5 with gMD and gMD for association, projection and thalamic WM fibers.

Trendwise associations were observed between some polygenic MDD risk scores and MD values in the anterior thalamic radiation, a group of WM fibers connecting frontal regions to anterior and middle nuclear groups of the thalamus (β = 0.084; p-FDR = 0.085, β = 0.088; p-FDR = 0.061, β = 0.086; p-FDR = 0.065 for PGRS threshold p ≤ 0.01, p ≤

0.05 and p ≤ 0.5 respectively) (Table S9, S10). Polygenic MDD, SCZ or BP risk was also not significantly associated with other individual tract-specific water diffusion measures in the sample including (N = 816) and excluding outliers (N = 733) (Table S7 and S9). These results were again similar for PGRS at the other thresholds (Table S8 and S10).

No significant PGRS * hemisphere interactions were found for the majority of individual tract water diffusion values, and therefore analyses on bilateral structures were conducted as a repeated measure. However, the SCZ-PGRS * hemisphere interaction was found to be significant for FA values in the parahippocampal portion of the cingulum (connecting orbitofrontal regions to the hippocampus) in the sample including outliers (β = −0.052; pcorrected = 0.032). We therefore conducted tests of this association with SCZ-PGRS separately for each lateralised structure for this region. However, these were also not significant for FA in either the left (β = −0.040; puncorrected = 0.262) or right (β = −0.079; puncorrected = 0.029) parahippocampal portion of the cingulum, following FDR correction.

Age-related effects in structural brain measures

We additionally performed a post-hoc validation analysis in order to verify that despite our lack of polygenic associations we could indeed replicate the widely reported negative associations between structural brain measures and age. We conducted our analyses using age as an independent variable and each of the brain measures as dependent variable. As expected, total GM and WM volumes showed a significant negative association with age, whereas this association was positive for total CSF volume (Table S11). Additionally, highly significant (β = −0.392 - −0.166; p-FDR < 0.001) negative associations were reported between age and the majority of subcortical volumes. Amygdala volumes were not significant associated with age (Table S12).

The association between age and WM microstructure has been examined in a larger sample (N = 3,513) of the UK Biobank dataset38. Age effects on WM in this sample with overlapping MRI and genetic data are broadly in agreement with this previous study, as indicated by a negative association between age and WM microstructure quality (Table S13 and S14).

Discussion

Structural brain abnormalities in corticostriatal and limbic structures are thought to play an important role in the pathophysiology of common disabling psychiatric disorders, such as MDD, SCZ and BP. Since these structural brain abnormalities have been observed in unaffected relatives of patients and have shown to be heritable quantitative traits, the current study sought to test the impact of the polygenic liability for MDD, SCZ, or BP on subcortical brain volumes and WM microstructure in individuals from a population-based study. Contrary to our predictions, we found no evidence for an association between polygenic risk for MDD, SCZ and BP and either subcortical brain regions or WM microstructure. These findings provide no support for the hypothesis that the polygenic liability for MDD, SCZ and BP is linked to structural brain changes in major psychiatric illness.

Our findings are in contrast to many published studies that report a relationship between subcortical GM volume and PGRS for psychiatric disorders24,25. However, these studies have generally been smaller than UK Biobank (N24/N35 = 294/274), increasing the risk of false positive results. In a complementary approach to our study, schizophrenia polygenic risk scores and linkage disequilibrium (LD) score regression have been used to test for shared genetic architecture between subcortical brain volumes and schizophrenia (N = 11,840)30. As in the current study, this study also reported a lack of overlapping genetic architecture between SCZ and subcortical volumes30. Moreover, Van Scheltinga et al.’s24 result of a significant association between and polygenic liability for SCZ and total brain volume could not be replicated in another study29. Holmes et al., (2012) (N = 438) also reported no association between MDD-PGRS and amygdala volume28. Here we extend these findings to SCZ, BD and MDD and across a range of subcortical measures (Table 1, 2, and S1-S4) using a large single scanner sample.

Only a few studies have been published regarding the association between genetic liability for psychiatric illness and WM integrity. No associations between WM integrity and polygenetic liability have been reported for BP, whereas polygenetic liability for MDD and SCZ was associated with decreased WM integrity26,27. Furthermore, individuals at high genetic risk for psychiatric disorders, such as relatives of patients, have shown reduced WM integrity40–42. Conversely, our study found no relation between genetic liability for psychiatric disorders and WM integrity (Table 3-5, and S5-S10).

The present study has some important limitations that should be taken into account. Firstly, it is possible that any shared genetic architecture with brain structure was too small to detect in the current sample. Although our sample sizes (Nvolumetric/NDTI = 978/816) are large for imaging research, they are relatively small when compared to a typical PGRS study. Moreover, our sample is a mixed community population, which included both healthy individuals, patients, and individuals without clinical records. To exclude the possibility of different genetic associations across clinical groups, future research should replicate our findings in cases and controls, separately. A general limitation of PGRS is that they account for only a small proportion of total phenotypic variance, far short of their narrow sense heritability. The addition of further discovery GWA samples for MDD, SCZ and BD will improve the predictive accuracy of PGRS and alongside developments in imaging consortia, such as ENIGMA (http://enigma.ini.usc.edu/), will make it possible to test for smaller effects in future studies43. The current results indicate that polygenic risk score associations with brain structure should be interpreted cautiously in smaller studies. Our findings should, however, be replicated to exclude the presence of a small genetic correlation between psychiatric disorder and brain structure and connectivity measures. Furthermore, future work should also examine whether the genetic architecture of other in vivo brain parameters, such as measures of cortical thickness and brain function overlap with major mental illness.

In summary, the current study reports no evidence of shared genetic architecture between psychiatric disorders and either subcortical brain volumes or WM integrity. Our results do not replicate the findings of several published studies with small sample sizes, but are supported by similar recent work, particularly in SCZ.

Author Contributions

AMM, HCW, SRC, XS and LMR contributed to the design of the study, analysis of the data, and writing the manuscript. MJA was involved in curating the data. IJD and MEB were involved in the conception of the study and overseeing analysis methodology. JG, EW and SPH were involved in the preprocessing and calculation of the polygenic risk scores. UK Biobank collected all data and was involved in the preprocessing of imaging data. All author discussed and commented on the manuscript.

Competing interests

AMM has previously received grant support from Pfizer, Lilly and Janssen. These studies are not connected to the current investigation. Remaining authors report no competing financial interests.

Corresponding author

Correspondence to Dr. Heather C. Whalley (heather.whalley{at}ed.ac.uk).

Acknowledgements

This work was supported by a grant of the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally’’ (STRADL) (reference 104036/Z/14/Z).

We thank the UK Biobank participants for their participation, and the UK Biobank team for their work in collecting and providing these data for analysis. Part of the work was undertaken in The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. Age UK (The Disconnected Mind project) also provided support for the work undertaken at CCACE.

LMR was supported by an Erasmus Traineeship Grant. XS receives support from China Scholarship Council. HCW is supported by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. SRC was supported by MRC grant MR/M013111/1. IJD is supported by the Medical Research Council award to CCACE (MR/K026992/1) and by the Dementias Platform UK (MR/L015382/1). IJD and SRC are additionally supported by the Age UK-funded Disconnected Mind project (http://www.disconnectedmind.ed.ac.uk).

References

  1. 1.↵
    McGrath, J., Saha, S., Chant, D. & Welham, J. Schizophrenia: A Concise Overview of Incidence, Prevalence, and Mortality. Epidemiol. Rev. 30, 67–76 (2008).
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    Fernandez-Pujals, A. M. et al. Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS). PLoS One 10, e0142197 (2015).
    OpenUrlCrossRefPubMed
  3. 3.↵
    Shih, R. A., Belmonte, P. L. & Zandi, P. P. A review of the evidence from family, twin and adoption studies for a genetic contribution to adult psychiatric disorders. http://dx.doi.org/10.1080/09540260400014401 16, 260–283 (2004).
  4. 4.↵
    Gejman, P. V., Sanders, A. R. & Duan, J. The Role of Genetics in the Etiology of Schizophrenia. Psychiatr. Clin. North Am. 33, 35–66 (2010).
    OpenUrlCrossRefPubMed
  5. 5.↵
    Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).
    OpenUrlCrossRefPubMed
  7. 7.↵
    Group, P. G. C. B. D. W. et al. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).
    OpenUrlCrossRefPubMed
  8. 8.↵
    Braber, A. den, Bohlken, M., Brouwer, R. & Ent, D. van’t. Heritability of subcortical brain measures: a perspective for future genome-wide association studies. Neuroimage 83, 98–102 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  9. 9.↵
    Kochunov, P., Jahanshad, N., Marcus, D. & Winkler, A. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage 111, 300–311 (2015).
    OpenUrlCrossRefPubMed
  10. 10.↵
    Hibar, D., Stein, J., Renteria, M. & Arias-Vasquez, A. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015).
    OpenUrlCrossRefPubMed
  11. 11.↵
    Zuliani, R. et al. Genetic variants in the ErbB4 gene are associated with white matter integrity. Psychiatry Res. - Neuroimaging 191, 133–137 (2011).
    OpenUrl
  12. 12.↵
    Arnone, D. et al. Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis. Br. J. Psychiatry 195, 194–201 (2009).
    OpenUrlAbstract/FREE Full Text
  13. 13.
    Haijma, S., Haren, N. Van, Cahn, W. & Koolschijn, P. Brain Volumes in Schizophrenia: A Meta-Analysis in Over 18 000 Subjects. Schizophr. Bull. 39, 1129–1138 (2012).
    OpenUrl
  14. 14.
    Hibar, D., Westlye, L., Thompson, P. & Andreassen, O. ENIGMA Bipolar disorder working group findings from 1,747 cases and 2,615 controls. Hum. Brain Mapp. (2014).
  15. 15.
    van Erp, T. G. M. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry 21, 547–553 (2016).
    OpenUrlCrossRefPubMed
  16. 16.
    Schmaal, L. et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol. Psychiatry 1–7 (2015). doi:10.1038/mp.2015.69
    OpenUrlCrossRefPubMed
  17. 17.↵
    Zhang, K. et al. Molecular, Functional, and Structural Imaging of Major Depressive Disorder. Neuroscience (2016).
  18. 18.↵
    Cullen, K. R. et al. Altered White Matter Microstructure in Adolescents With Major Depression: A Preliminary Study. J. Am. Acad. Child Adolesc. Psychiatry 49, 173–183.e1 (2010).
    OpenUrlPubMedWeb of Science
  19. 19.
    Korgaonkar, M., Grieve, S., Koslow, S. & Gabrieli, J. Loss of White Matter Integrity in Major Depressive Disorder: Evidence Using Tract-Based Spatial Statistical Analysis of Diffusion Tensor Imaging. Hum. Brain Mapp. 32, 2161–2171 (2011).
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.
    Zhang, A. et al. Quantitative tract-specific measures of uncinate and cingulum in major depression using diffusion tensor imaging. Neuropsychopharmacology 37, 959–67 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  21. 21.
    Anderson, D., Ardekani, B. & Burdick, K. Overlapping and distinct gray and white matter abnormalities in schizophrenia and bipolar I disorder. Bipolar 15, 680–693 (2013).
    OpenUrl
  22. 22.
    Lener, M., Wong, E., Tang, C., Byne, W. & Goldstein, K. White Matter Abnormalities in Schizophrenia and Schizotypal Personality Disorder. Schizophr. Bull. (2014).
  23. 23.↵
    Roalf, D., Gur, R., Verma, R. & Parker, W. White matter microstructure in schizophrenia: Associations to neurocognition and clinical symptomatology. Schizophrenia 161, 42–49 (2015).
    OpenUrl
  24. 24.↵
    Terwisscha Van Scheltinga, A. F. et al. Genetic schizophrenia risk variants jointly modulate total brain and white matter volume. Biol. Psychiatry 73, 525–531 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  25. 25.↵
    Caseras, X., Tansey, K. E., Foley, S. & Linden, D. Association between genetic risk scoring for schizophrenia and bipolar disorder with regional subcortical volumes. Transl. Psychiatry 5, 1–5 (2015).
    OpenUrl
  26. 26.↵
    Bohlken, M. M. et al. Structural Brain Connectivity as a Genetic Marker for Schizophrenia. JAMA Psychiatry 73, 11–19 (2016).
    OpenUrl
  27. 27.↵
    Whalley, H. C. et al. Polygenic risk and white matter integrity in individuals at high risk of mood disorder. Biol. Psychiatry 74, 280–286 (2014).
    OpenUrl
  28. 28.↵
    Holmes, A. J. et al. Individual differences in amygdala-medial prefrontal anatomy link negative affect, impaired social functioning, and polygenic depression risk. J. Neurosci. 32, 18087–18100 (2012).
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    Papiol, S., Mitjans, M., Assogna, F. & Piras, F. Polygenic determinants of white matter volume derived from GWAS lack reproducibility in a replicate sample. Transl. Psychiatry 4, 1–4 (2014).
    OpenUrl
  30. 30.↵
    Franke, B. et al. Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nat. Neurosci. 19, 420–431 (2016).
    OpenUrlCrossRefPubMed
  31. 31.
    Holmes, A., Lee, P., Hollinshead, M. & Bakst, L. Individual Differences in Amygdala-Medial Prefrontal Anatomy Link Negative Affect, Impaired Social Functioning, and Polygenic Depression Risk. J (2012).
  32. 32.↵
    Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  33. 33.↵
    Hussein, S. M. I. et al. Genome-wide characterization of the routes to pluripotency. Nature 516, 198–206 (2014).
    OpenUrlCrossRefPubMed
  34. 34.↵
    Hagenaars, S. P. et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol. Psychiatry 1–4 (2016). doi:10.1038/mp.2015.225
    OpenUrlCrossRef
  35. 35.↵
    Euesden, J., Lewis, C. M. & Reilly, P. F. O. Genome analysis PRSice: Polygenic Risk Score software. 31, 1466–1468 (2015).
    OpenUrl
  36. 36.↵
    Ripke, S. et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).
    OpenUrlCrossRefPubMedWeb of Science
  37. 37.↵
    Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–52 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    S.R., Cox, S.J., Ritchie, E.M., Tucker-Drob, D.C., Liewald, S.P., Hagenaars, G., Davies, J.M., Wardlaw, C.R., Gale, M.E., Bastin, I.J., D.. Age differences in brain white matter microstructure in UK Biobank (N = 3,513). bioRxiv 1–34 (2016). doi:10.1101/051771
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    Benjamini, Y. Hochberg, Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. J.R. Stat. Soc. B 57, 289–300 (1995).
    OpenUrl
  40. 40.↵
    Sprooten, E. et al. White matter integrity in individuals at high genetic risk of bipolar disorder. Biol. Psychiatry 70, 350–356 (2011).
    OpenUrlPubMed
  41. 41.
    Huang, H., Fan, X., Williamson, D. E. & Rao, U. White matter changes in healthy adolescents at familial risk for unipolar depression: a diffusion tensor imaging study. Neuropsychopharmacology 36, 684–91 (2011).
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.↵
    Munoz Maniega, S. et al. A diffusion tensor MRI study of white matter integrity in subjects at high genetic risk of schizophrenia. Schizophr. Res. 106, 132–139 (2008).
    OpenUrlCrossRefPubMed
  43. 43.↵
    Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).
    OpenUrlCrossRefPubMedWeb of Science
  44. 44.
    Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012).
    OpenUrlCrossRefPubMedWeb of Science
  45. 45.
    Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002).
    OpenUrlCrossRefPubMedWeb of Science
  46. 46.
    Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).
    OpenUrlCrossRefPubMedWeb of Science
  47. 47.
    Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
    OpenUrlCrossRefPubMedWeb of Science
  48. 48.
    Andersson, J. L. R., Jenkinson, M. & Smith, S. M. Non-linear optimisation. FMRIB technical report TR07JA1. In Pract. 16 (2007).
  49. 49.
    Andersson, J. L. R., Jenkinson, M. & Smith, S. Non-linear registration aka Spatial normalisation FMRIB Technial Report TR07JA2. In Pract. 22 (2007).
  50. 50.
    Patenaude, B., Smith, S. M., Kennedy, D. & Jenkinson, M. NIH Public Access. Neuroimage 56, 907–922 (2012).
    OpenUrl
  51. 51.
    Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S. & Woolrich, M. W. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34, 144–155 (2007).
    OpenUrlCrossRefPubMedWeb of Science
  52. 52.
    Groot, M. De. Cross-Subject Image Analysis in Diffusion Brain MRI Marius de Groot.
  53. 53.
    Basser, P. J., Mattiello, J. & LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–67 (1994).
    OpenUrlCrossRefPubMedWeb of Science
  54. 54.
    Pierpaoli, Carlo, et al. Diffusion tensor MR imaging of the human brain. 259–267 (1996).
  55. 55.
    Cercignani, M., Inglese, M., Pagani, E., Comi, G. & Filippi, M. Mean Diffusivity and Fractional Anisotropy Histograms of Patients with Multiple Sclerosis. AJNR Am J Neuroradiol. 22, 952–958 (2001).
    OpenUrlAbstract/FREE Full Text
  56. 56.
    Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don'ts of diffusion MRI. Neuroimage 73, 239–54 (2013).
    OpenUrlCrossRefPubMedWeb of Science
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Posted October 12, 2016.
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Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank
LM Reus, X Shen, J Gibson, E Wigmore, L Ligthart, MJ Adams, G Davies, SR Cox, SP Hagenaars, ME Bastin, IJ Deary, HC Whalley, AM McIntosh
bioRxiv 080283; doi: https://doi.org/10.1101/080283
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