Common genetic variants influence human subcortical brain structures

Abstract

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Extended Data Figure 1. Outline of the genome-wide association meta-analysis

Structural T₁-weighted brain MRI and biological specimens for DNA extraction were acquired from each individual at each site. Imaging protocols were distributed to and completed by each site for standardized automated segmentation of brain structures and calculation of the ICV. Volumetric phenotypes were calculated from the segmentations. Genome-wide genotyping was completed at each site using commercially available chips. Standard imputation protocols to the 1000 Genomes reference panel (phase 1, version 3) were also distributed and completed at each site. Each site completed genome-wide association for each of the eight volumetric brain phenotypes with the listed covariates. Statistical results from GWAS files were uploaded to a central site for quality checking and fixed effects meta-analysis.

Extended Data Figure 2. Ancestry inference via multi-dimensional scaling plots

Multi-dimensional scaling (MDS) plots of the discovery cohorts to HapMap III reference panels of known ancestry are displayed. Ancestry is generally homogeneous within each group. In all discovery samples any individuals with non-European ancestry were excluded before association. The axes have been flipped to the same orientation for each sample for ease of comparison. ASW, African ancestry in southwest USA; CEU, Utah residents with northern and western European ancestry from the CEPH collection; CHD, Chinese in metropolitan Denver, Colorado; GIH, Gujarati Indians in Houston, Texas; LWK, Luhya in Webuye, Kenya; MEX, Mexican ancestry in Los Angeles, California; MKK, Maasai in Kinyawa, Kenya; TSI, Tuscans in Italy; YRI, Yoruba in Ibadan, Nigeria.

Extended Data Figure 3. Genomic function is annotated near novel genome-wide significant loci

a–h, For each panel, zoomed-in Manhattan plots (±400 kb from top SNP) are shown with gene models below (GENCODE version 19). Plots below are zoomed to highlight the genomic region that probably contains the causal variant(s) (r² > 0.8 from the top SNP). Genomic annotations from the UCSC browser and ENCODE are displayed to indicate potential functionality (see Methods for detailed track information). SNP coverage is low in f owing to a common genetic inversion in the region. Each plot was made using the Locus Track software (http://gump.qimr.edu.au/general/gabrieC/LocusTrack/).

Extended Data Figure 4. Quantile–quantile and forest plots from meta-analysis of discovery cohorts

a, Quantile–quantile plots show that the observed P values only deviate from the expected null distribution at the most significant values, indicating that population stratification or cryptic relatedness are not unduly inflating the results. This is quantified through the genomic control parameter (λ; which evaluates whether the median test statistic deviates from expected). λ values near 1 indicate that the median test statistic is similar to those derived from a null distribution. Corresponding meta-analysis Manhattan plots can be found in Fig. 1. b, Forest plots show the effect at each of the contributing sites to the meta-analysis. The size of the dot is proportional to the sample size, the effect is shown by the position on the x axis, and the standard error is shown by the line. Sites with an asterisk indicate the genotyping of a proxy SNP (in perfect linkage disequilibrium calculated from 1000 Genomes) for replication.

Extended Data Figure 5. Influence of patients with neuropsychiatric disease, age and gender on association results

a, Scatterplot of effect sizes including and excluding patients with neuropsychiatric disorders for nominally significant SNPs. For each of the eight volumetric phenotypes, SNPs with P < 1 × 10⁻⁵ in the full discovery set meta-analysis were also evaluated excluding the patients. The beta values from regression, a measure of effect size, are plotted (blue dots) along with a line of equivalence between the two conditions (red line). The correlation between effect sizes with and without patients was very high (r > 0.99), showing that the SNPs with significant effects on brain structure are unlikely to be driven by the diseased individuals. b, Meta-regression comparison of effect size with mean age at each site. Each site has a corresponding number and coloured dot in each graph. The size of each dot is based on the standard error such that bigger sites with more definitive estimates have larger dots (and more influence on the meta-regression). The age range of participants covered most of the lifespan (9–97 years), but only one of these eight loci showed a significant relationship with the mean age of each cohort (rs608771 affecting putamen volume). c, Meta-regression comparison of effect size with the proportion of females at each site. No loci showed evidence of moderation by the proportion of females in a given sample. However, the proportion of females at each site has a very restricted range, so results should be interpreted with caution. Plotted information follows the same convention as described in b. The sites are numbered in the following order: (1) AddNeuroMed, (2) ADNI, (3) ADNI2GO, (4) BETULA, (5) BFS, (6) BIG, (7) BIG-Rep, (8) BrainSCALE, (9) BRCDECC, (10) CHARGE, (11) EPIGEN, (12) GIG, (13) GSP, (14) HUBIN, (15) IMAGEN, (16) IMpACT, (17) LBC1936, (18) Lieber, (19) MAS, (20) MCIC, (21) MooDS, (22) MPIP, (23) NCNG, (24) NESDA, (25) neuroIMAGE, (26) neuroIMAGE-Rep, (27) NIMH, (28) NTR-Adults, (29) OATS, (30) PAFIP, (31) QTIM, (32) SHIP, (33) SHIP-TREND, (34) SYS, (35) TCD-NUIG, (36) TOP, (37) UCLA-BP-NL and (38) UMCU.

Extended Data Figure 6. Cross-structure analyses

a, Radial plots of effect sizes from the discovery sample for all genome-wide significant SNPs identified in this study. Plots indicate the effect of each genetic variant, quantified as percentage variance explained, on the eight volumetric phenotypes studied. As expected, the SNPs identified with influence on a phenotype show the highest effect size for that phenotype: putamen volume (rs945270, rs62097986, rs608771 and rs683250), hippocampal volume (rs77956314 and rs61921502), caudate volume (rs1318862) and ICV (rs17689882). In general much smaller effects are observed on other structures. b, Correlation heat map of GWAS test statistics (t-values) and hierarchical clustering. Independent SNPs were chosen within an linkage disequilibrium block based on the highest association in the multivariate cross-structure analysis described in Extended Data Fig. 6c. Two heat maps are shown taking only independent SNPs with either P < 1 × 10⁻⁴ (left) or P < 0.01 (right) in the multivariate cross-structure analysis. Different structures are labelled in developmentally similar regions by the colour bar on the top and side of the heat map including basal ganglia (putamen, pallidum, caudate and accumbens; blue), amygdalo–hippocampal complex (hippocampus and amygdala; red), thalamus (turquoise) and ICV (black). Hierarchical clustering showed that developmentally similar regions have mostly similar genetic influences across the entire genome. The low correlation with the ICV is owing to it being used as a covariate in the subcortical structure GWAS associations. c, A multivariate cross-structure analysis of all volumetric brain traits. A Manhattan plot (left) and corresponding quantile–quantile plot (right) of multivariate GWAS analysis of all traits (volumes of the accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and ICV) in the discovery data set using the TATES method is shown. Multivariate cross-structure analysis confirmed the univariate analyses (see Table 1), but did not reveal any additional loci achieving cross-structure levels of significance.

Extended Data Figure 7. Pathway analysis of GWAS results for each brain structure

A pathway analysis was performed on each brain volume GWAS using KGG to conduct gene-based tests and the Reactome database for pathway definition. Pathway-wide significance was calculated using a Bonferroni correction threshold accounting for the number of pathways and traits tested such that P_thresh = 0.05/(671 pathways × 7 independent traits) = 1.06 × 10⁻⁵ and is shown here as a red line. The number of independent traits was calculated by accounting for the non-independence of each of the eight traits examined (described in the Methods). Variants that influence the putamen were clustered near genes known to be involved in DSCAM interactions, neuronal arborization and axon guidance. Variants that influence intracranial volume are clustered near genes involved in EGFR and phosphatidylinositol-3-OH kinase (PI(3)K)/AKT signalling pathways, known to be involved in neuronal survival. All of these represent potential mechanisms by which genetic variants influence brain structure. It is important to note that the hybrid set-based test (HYST) method for pathway analysis used here can be strongly influenced by a few highly significant genes, as was the case for putamen hits in which DCC and BCL2L1 were driving the pathway results.

Extended Data Figure 8. Spatio-temporal maps showing expression of genes near the four significant putamen loci over time and throughout regions of the brain

Spatio-temporal gene expression was plotted as normalized log₂ expression. Different areas of the neocortex (A1C, primary auditory cortex; DFC, dorsolateral prefrontal cortex; IPC, posterior inferior parietal cortex; ITC, inferior temporal cortex; MFC, medial prefrontal cortex; M1C, primary motor cortex; OFC, orbital prefrontal cortex; STC, superior temporal cortex; S1C, primary somatosensory cortex; VFC, ventrolateral prefrontal cortex; V1C, primary visual cortex) as well as subcortical areas (AMY, amygdala; CBC, cerebellar cortex; HIP, hippocampus; MD, mediodorsal nucleus of the thalamus; STR, striatum) are plotted from 10 post-conception weeks (PCW) to more than 60 years old. Genes that probably influence putamen volume are expressed in the striatum at some point during the lifespan. After late fetal development, KTN1 is expressed in the human thalamus, striatum and hippocampus and is more highly expressed in the striatum than the cortex. Most genes seem to have strong gradients of expression across time, with DCC most highly expressed during early prenatal life, and DLG2 most highly expressed at mid-fetal periods and throughout adulthood. BCL2L1, which inhibits programmed cell death, has decreased striatal expression at the end of neurogenesis (24–38 PCW), a period marked by increased apoptosis in the putamen.

Extended Data Figure 9. CTCF-binding sites in the vicinity of the putamen locus marked by rs945270

CTCF-binding sites from the ENCODE project are displayed from the database CTCFBSDB 2.0 (ref. 23) from two different cell types: embryonic stem cells (track ENCODE_Broad_H1-hESC_99540) and a neuroblastoma cell line differentiated with retinoic acid (ENCODE_UW_SK-N-SH_RA_97826). A proxy SNP to the top hit within the locus, rs8017172 (r² = 1.0 to rs945270), lies within a CTCF-binding site called based on ChIP-seq data in the embryonic stem cells and near the binding site in neural SK-N-SH cells. As this is the lone chromatin mark in the intergenic region (see Extended Data Fig. 3), it suggests that the variant may disrupt a CTCF-binding site and thereby influence transcription of surrounding genes.

Extended Data Figure 10. Shape analysis in 1,541 young healthy subjects shows consistent deformations of the putamen regardless of segmentation protocol

a, b, The distance from a medial core to surfaces derived from FSL FIRST (a; identical to Fig. 2c) or FreeSurfer (b) segmentations was derived in the same 1,541 subjects. Each copy of the rs945270-C allele was significantly associated with an increased width in coloured areas (false discovery rate corrected at q = 0.05) and the degree of deformation is labelled by colour. The orientation is indicated by arrows. A, anterior; I, inferior; P, posterior; S, superior. Shape analysis in both software suites gives statistically significant associations in the same direction. Although the effects are more widespread in the FSL segmentations, FreeSurfer segmentations also show overlapping regions of effect, which appears strongest in anterior and superior sections.

Extended Data Figure 11. The phenotypic variance explained by all common variants in this study

a, Twin-based heritability (with 95% confidence intervals), measuring additive genetic influences from both common and rare variation, is shown for comparison with common variant based heritability (see Methods). b, The median estimated percentage of phenotypic variance explained by all SNPs (and 95% confidence interval) is given for each brain structure studied. The full genome-wide association results from common variants explain approximately 7–15% of variance depending on the phenotype. c, The median estimated variance explained by each chromosome is shown for each phenotype. d, Some chromosomes explain more variance than would be expected by their length, for example chromosome 18 in the case of the putamen, which contains the DCC gene.

Figure 1. Common genetic variants associated with subcortical volumes and the ICV

Manhattan plots coloured with a scheme that matches the corresponding structure (middle) are shown for each subcortical volume studied. Genome-wide significance is shown for the common threshold of P = 5 × 10⁻⁸ (grey dotted line) and also for the multiple comparisons-corrected threshold of P = 7.1 × 10⁻⁹ (red dotted line). The most significant SNP within an associated locus is labelled.

Figure 2. Effect of rs945270 on KTN1 expression and putamen shape

a, b, Expression quantitative trait loci study in brain tissue demonstrates the effect of rs945270 on KTN1 gene expression in frontal cortex tissue from 304 subjects from the North American Brain Expression Cohort (NABEC) (a) and in an independent sample of 134 subjects from the UK Brain Expression Cohort (UKBEC) (b), sampled from both frontal cortex and putamen. Boxplot dashed bars mark the twenty-fifth and seventy-fifth percentiles. c, Surface-based analysis demonstrates that rs945270 has strong effects on the shape of superior and lateral portions of the putamen in 1,541 subjects. Each copy of the rs945270-C allele was significantly associated with increased width in coloured areas (false discovery rate corrected at q = 0.05), and the degree of deformation is labelled by colour, with red indicating greater deformation. Orientation is indicated by arrows. A, anterior; I, inferior; P, posterior, S, superior.