G-protein genomic association with normal variation in gray matter density

INTRODUCTION

Studying associations between genetic variables and imaging traits is a valuable strategy, a maturing field known as imaging genetics, which holds the promise to help better understand the genetic underpinnings of cognition and reveal biological mechanisms of mental disorders [Thompson et al., 2014]. Structural magnetic resonance imaging (sMRI) provides a noninvasive approach to study the morphology of the living brain. Quantitative measures are derived from T₁-weighted MRI images using computational methods such as voxel-based morphometry (VBM) [Ashburner and Friston, 2005] or cortical surface reconstruction (FreeSurfer) [Fischl and Dale, 2000] to depict various structural features, including brain volume, gray matter volume, cortical thickness, and surface area. These features can then be compared at subject level for their functional implications. Under this strategy, biomarkers have been consistently identified from both healthy and diseased human brains, characterizing neural development, ageing, Alzheimer's disease, schizophrenia, and so on [Ellison-Wright et al., 2008; Frisoni et al., 2010; Giedd and Rapoport, 2010; Raz and Rodrigue, 2006]. More importantly, many attributes of brain structure, including both global and regional traits, are confirmed to be genetically influenced in twin studies, with heritability estimated around 40–90% [Peper et al., 2007; Thompson et al., 2001; Winkler et al., 2010].

Heterotrimeric guanine nucleotide-binding proteins (G-proteins) serve as molecular switches in intracellular singling cascades, where they sense signals from cell-surface receptors that are activated by extracellular stimuli and transduce signals to downstream effectors [Oldham and Hamm, 2008]. It has been documented that activated G-proteins directly interact with a variety of effector proteins, including phosphodiesterase E, phospholipase D, phospholipase C, inducible nitric oxide synthase, calcium channels, and the G protein-regulated inducer of neurite outgrowth 1 and 2 [Cabrera-Vera et al., 2003]. Particularly, G-proteins are richly expressed in the brain and involved in cortical development, neuronal growth as well as neuronal signaling [Bromberg et al., 2008; Hamm, 1998; Offermanns, 2001]. For instance, there is evidence that G protein alpha 12 and alpha 13 can mediate growth cone collapse and neurite retraction [Nurnberg et al., 2008], while deficiency of the G-protein α-subunits causes localized overmigration of neurons in the developing cerebral and cerebellar cortices [Moers et al., 2008]. Knockout of G protein beta 5 impairs brain development and causes multiple neurologic abnormalities in mice [Zhang et al., 2011]. Neural expression of G protein-coupled receptor 3, 6, and 12 has been implicated in up-regulating cyclic AMP (cAMP) levels in neurons and stimulating neurite outgrowth [Tanaka et al., 2007].

Given their roles in neural development, G-proteins pose promising candidates for imaging genetic association studies. More recently, Chavarria-Siles et al. [2013] investigated 502 single nucleotide polymorphisms (SNPs) in 25 G-protein genes for their associations with brain-wide gray matter volume using a mass univariate model. They identified seven SNPs to be significantly associated with gray matter volume variation in different brain regions, including the medial frontal cortex. While this work provides direct evidence that G-protein SNPs are related to brain structure, the univariate analysis is not able to assess the aggregate effects of multiple variants. In addition, considering that G-proteins are key cell signaling molecules and affect multiple biological processes, their effect on neurobiological conditions is likely not isolated but involves a large network. Therefore, a multivariate study is strongly encouraged to search for G-protein involved genetic components that may better delineate the basis of the gray matter variation.

In the present work, we applied a semi-blind multivariate approach, parallel independent component analysis with reference (pICA-R) [Chen et al., 2013], to explore G-protein involved genomic factors underlying brain structure in a large homogeneous cohort of 1,256 healthy Caucasians. Brain-wide gray matter density (GMD) images were analyzed in conjunction with genome-wide SNP data. G-proteins identified in the work by Chavarria-Siles et al. [2013] served as references to guide the analysis but the method still allows for other genetic variants to be revealed as well.

MATERIALS AND METHODS

Association Analysis

pICA-R [Chen et al., 2013] was employed to identify relationships between hidden factors of particular attribute within two data modalities, as illustrated in Figure 1. In this work, pICA-R estimates underlying components for the GMD images (Modality 1) and the SNP data (Modality 2) independently and in parallel. The data decomposition builds upon the regular infomax ICA framework [Bell and Sejnowski, 1995], which identifies sets of co-varying variables that are independent from each other and organizes them into different components, as described in Eq. 1, where X represents the observed data matrix (subject × voxel/SNP); S, A, and W denote the component, loading/mixing, and unmixing matrix, respectively. The subscript d runs from 1 to 2, denoting the data modality.

(1)

Mathematically, pICA-R iteratively updates the unmixing matrices W₁ and W₂ to gradually optimize the objective functions F₁, F₂, and F₃, as described in Eqs. 2 and 3. F₁ is the objective function of the regular infomax for Modality 1, where independence among components is achieved by maximizing the entropy H. f_y(Y) is the probability density function of the sigmoid function Y. E is the expected value and W₀ is the bias vector. In contrast, F₂ is the objective function for Modality 2, where an additional Euclidean distance metric is imposed to extract maximally independent components, one of which also closely resembles the reference r. To avoid false positives, the constraint weight λ is adaptively adjusted so that the distance metric is not over-emphasized. The inter-modality correlation function F₃ is designed to maximize the correlations computed over the columns of the loading matrices A₁ and A₂, capturing connections between pairs of inter-modality components.

(2)

(3)

The reference r is a binary vector with the same number of loci as the genomic data, where the selected referential loci are set to “1” and the rest are “0”s. This binary reference serves as a mask such that the distance between the component and reference vector is optimized particularly for the referential loci only. This design enables a semi-blind decomposition, where the presumed causal loci are constrained to be highlighted in the resulting component while the remaining loci are allowed to show their own importance driven by the data. For a full description of the mathematical details of pICA-R, we refer readers to the original publication [Chen et al., 2013].

Specifically in this work, the genetic references were derived from genes encoding heterotrimeric G-proteins. We leveraged the results from an imaging genetics study reporting associations between gray matter volume variations and SNPs in G-protein coding genes [Chavarria-Siles et al., 2013]. Among the seven genes identified in that previous work, GNA15, GNAO1, and GNB5 covered less than 10 SNPs in our data, therefore were not tested as references given that simulation results suggested an optimal reference of 20 true causal loci [Chen et al., 2013]. For the remaining four genes, GNG2, GNAQ, GNA14, and GNAL, we identified the corresponding SNPs in each gene and grouped neighboring SNPs with moderate correlations (r² > 0.2, [Ripke et al., 2011]) into a cluster. The largest cluster within each gene was then used as the reference. This is because SNPs in one gene do not necessarily contribute simultaneously to only a single component, which is against the design of pICA-R. Instead, SNPs in one LD cluster are more likely to contribute to the same component and serve as good candidates for reference given that a marker allele in LD with the causal variant should show (by proxy) an association with the trait of interest [Stranger et al., 2011]. In this way, we tested four references hosted by GNG2, GNAQ, GNA14, and GNAL.

Like infomax ICA, pICA-R requires estimating the number of components before data decomposition. Minimum description length (MDL) [Rissanen, 1978] is commonly used for the imaging modality to select the component number yielding the most efficient representation of the original data. However in the SNP data, most genetic factors account for small amounts of variance, except for those related to population structure. Thus, MDL is much less applicable to select the component number such that the major variance in the data is retained. Instead, we chose to estimate the component number based on components’ consistency, to obtain the most stable data decomposition [Chen et al., 2012].

Validation

The genetic references were implicated in the work by Chavarria-Siles et al. [2013] which used a portion of the current BIG data. Unfortunately, we did not have a detailed list of the subjects used in that work and could not investigate whether our finding would hold in the non-overlapping portion of the BIG data. Instead, we used the independent MCIC data to assess the validity of the imaging genetic associations identified in the BIG data. As pICA-R's performance would significantly degrade given the MCIC sample size of 89 and 666,019 SNPs [Chen et al., 2013], we chose to project the sMRI and SNP components identified in the BIG data to the MCIC data to obtain the new loading coefficients, as described in Eq. 4:

(4)

where the subscript “d” runs from 1 to 2, denoting the sMRI and SNP modality, respectively. represents the pseudo inverse of the component matrix extracted from the BIG data. and denote respectively the submatrices of and the MCIC observed data, corresponding to the top voxels or SNPs presenting relatively stronger correlations with the loadings. A^MCICrepresents the loaing matrix estimated through projecting the top voxels or SNPs. We expect that the loadings obtained in this way should more accurately reflect the effects of the most important markers. Finally, the correlations between the projected loadings were calculated to assess whether the sMRI-SNP association identified in the BIG data remained significant in the MCIC data.

RESULTS

The number of components was estimated to be 10 and 11 for the sMRI and SNP data, respectively. Among all the tested references, the one derived from GNA14 elicited a significant sMRI-SNP association (r = −0.16, P = 2.27 × 10⁻⁸). This exceeded the conservative Bonferroni threshold of 1.14 × 10⁻⁴ which corrected for all the tested four genetic references and the combinations of all extracted components (10 sMRI × 11 SNP) in each run. The reference comprised 22 out of 83 SNPs from GNA14, as listed in Supporting Information Table S1. These SNPs are in moderate LD, presenting a mean r-square of 0.54, and distanced by an average of 2,557 base pairs. The sMRI-SNP association remained significant, exhibiting a correlation of −0.16 (P = 2.34 × 10⁻⁸) when the SNP component was controlled for age and sex, as shown in Figure 2a. Besides, the SNP-sMRI association remained significant (P = 3.30 × 10⁻⁷) when the top three principal components of the SNP data were further included as covariates [Ripke et al., 2011], indicating that the observed association was not likely attributable to ancestral background. The associated sMRI component was thresholded at |Z| > 2 for top voxels. The number of top SNPs was determined based on the absolute values of the component z-scores using a more conservative linear fitting approach (for details see Supporting Information Figure S1), yielding a total of 2,000 top SNPs corresponding to |Z| > 3.37. We then projected the top voxels and SNPs to the MCIC data, and the resulting loadings again yielded a significant correlation of −0.25 (P = 0.02) when controlling for age and sex, as shown in Figure 2b. To be noted, the projected association was robust to the threshold selection, remaining significant with a number of top SNPs ranging from 1,000 to 2,500. No association was observed between the identified sMRI/SNP component and the reversal learning score.

The top contributing voxels of the sMRI component were further mapped to nearest gray matter to obtain the Talairach atlas labels [Lancaster et al., 1997, 2000]. Figure 3 and Table 3 show the spatial map and the mapped regions, respectively. It can be seen that the identified brain network comprised superior and medial frontal gyri, precuneus, and cingulate gyrus. For the SNP component, Supporting Information Table S2 provides a summary of the top contributing SNPs, including chromosome, base pair position, corresponding gene, minor allele, and component z-score. From the GNA14 reference, five SNPs were identified as top contributors to the associated SNP component, including rs10869927_G (“G” for the minor allele), rs10781441_T, rs12684903_A, rs2889774_T, and rs7047853_G. Figure 4 shows a Manhattan plot of weights of loci for the identified SNP component, where clusters are marked if they span more than five SNPs with maximum normalized weights greater than 5. Out of the top 2,000 SNPs, 803 are intragenic and mapped to 272 unique genes, including another G-protein, GNA12, and two G-protein-coupled receptors, GPR113 and GPR133. IPA revealed that these 272 genes were significantly enriched in a number of canonical pathways, including synaptic long-term potentiation (LTP) and depression (LTD), axonal guidance, protein kinase A (PKA), CREB, and nNOS signaling, as listed in Table 4. In particular, all the pathways highlighted in bold remained significantly enriched when the number of top SNPs was adjusted from 1,000 to 2,500, indicating a relatively consistent genetic architecture. We also explored through IPA potential interactions between selected genes of interest, including GNA12, GNA14, GRM1, PRKCD, CAMK2B, and PDIA3. The constructed network is illustrated in Supporting Information Figure S2.

DISCUSSION

In this work, we applied pICA-R to explore genomic basis of brain structure in a homogeneous cohort of healthy Caucasians. G-proteins arose as potential references given that they are implicated in neural development. A previous univariate study [Chavarria-Siles et al., 2013] provided more direct evidence for associations between G-protein SNPs and regional gray matter variations. We then leveraged these results and derived four genetic references which were then investigated in the semi-blind multivariate framework. In the BIG dataset which included 1,256 subjects, a GNA14 reference elicited a significant SNP-sMRI association (r = −0.16, P = 2.34 × 10⁻⁸), while the other three tested references did not yield any significant finding. As discussed by Chen et al. [2013], references derived from univariate models do not always yield significant findings in multivariate analyses. This might be due to various factors. One possibility is that the reference SNPs may have heterogeneous effects and contribute to different components. Or the related network as a whole may not be well represented in this specific dataset.

Using a semi-blind multivariate approach, we identified extended SNP and sMRI components compared to the previous work by Chavarria-Siles et al. The identified brain network consisted of medial frontal gyrus, superior frontal gyrus, precuneus, and cingulate regions. To be noted, the medial frontal cortex was also identified by the Chavarria-Siles et al. that employed 532 BIG subjects and used a different method. Regarding the genetic modality, five reference SNPs from GNA14, as expected, were identified as top contributors to the associated SNP component. The SNP highlighted in Chavarria-Siles et al. (rs4745639) was not included in the reference, however it showed moderate LD with rs2889774 and rs10869927 (r² of 0.15 and 0.14, respectively), and all contributed with negative component weights. The elicited SNP component was enriched in pathways related to neuronal functions, as listed in Table 4. More importantly, the identified association was further replicated in the independent MCIC dataset. Compared to univariate analyses, pICA-R assesses multiple variables for the aggregate effect. For the SNP modality, pICA-R is well posed to model polygenicity and to capture the additive effect of multiple SNPs with moderate individual effects [Chen et al., 2013; Polderman et al., 2015]. For the sMRI modality, pICA-R extracts patterns of variations shared by regional and distant voxels [Xu et al., 2009]. And imaging genetic associations are then evaluated between the SNP and sMRI components’ loadings. Thus, our results delineate a genomic basis underlying a proportion of the GMD variation in the highlighted frontal and parietal regions.

The spatial map of the identified sMRI component clearly highlighted the midline of brain, which is frequently implicated in neural development, ageing, and cognition [Frangou et al., 2004; Lyuksyutova et al., 2003; Shaw et al., 2006; Sowell et al., 2003]. Prefrontal cortex is known to be involved in complex cognitive behavior and decision making [Koechlin and Hyafil, 2007; Koechlin et al., 2003]. Precuneus plays a key role in highly integrated tasks, including episodic memory retrieval, self-referential processes, and consciousness [Laureys et al., 2004; Lundstrom et al., 2005; Ochsner et al., 2004]. Cingulate cortex is an integral part of the limbic system and active in a variety of cognitive functions such as emotion, learning and memory [Bush et al., 2000]. More interestingly, these regions are also among those showing relatively significant structural variations for different age and intelligence groups. A VBM study demonstrated positive correlations between IQ and GMD in voxel clusters distributed along the brain midline, including frontal cortex, cingulate, and precuneus [Frangou et al., 2004]. At superior and medial frontal gyri, the trajectories of cortical thickness change with age are found to be different between the superior and the high/average intelligence groups [Shaw et al., 2006]. Besides, axons extend on either side of the midline (anterior–posterior axis) to form longitudinal tracts and the midline cells are crucial for guiding axon outgrowth [Lyuksyutova et al., 2003]. These observations suggest that the identified brain network captures regional GMD co-variations that can play a role in cognitive abilities and might reflect inter-individual differences in brain development.

The SNP component was overrepresented in a number of pathways communicating with each other and with G-proteins. From those shown in Table 4, LTP and LTD are two forms of synaptic plasticity that affect signal transmission between neurons and have been widely studied to understand the mechanisms underlying learning and memory [Martin et al., 2000; Neves et al., 2008]. It is believed that glutamate receptors are major triggers for the induction of LTP and LTD, where PKA, protein kinase C (PKC), calcium/calmodulin-dependent protein kinase II (CaMKII) can all play a role [Collingridge et al., 2004]. CREB is majorly activated by cAMP through PKA [Delghandi et al., 2005]. In particular, a behavioral experience can elicit synaptic activities which activate CREB, inducing expression of molecules contributing to consolidating changes in synaptic strength [Benito and Barco, 2010]. Axon guidance strongly relies on a number of cue molecules, among which netrins are the most well understood. It has been demonstrated that netrins retain the function of attracting axons toward the brain midline while also repelling some axons, and responses to the guidance cue netrin-1 (NTN1) are sensitive to levels of cAMP or PKA activity [Dickson, 2002]. Neuronal nitric oxide synthase (nNOS) is a biosynthetic enzyme functioning in several types of synaptic plasticity, including LTP and LTD [Bon and Garthwaite, 2003; Nelson et al., 1995]. The phosphorylation of nNOS is regulated by kinases and phosphatases such as PKA, PKC, and CaMKII [Zhou and Zhu, 2009]. It is noteworthy that G-proteins are involved in the signaling cascades of all these cellular machineries, including glutamate, cAMP, PKA, PKC, CREB, and CaMKII [Neves et al., 2002].

Among the enriched pathways, overlaps of genes were observed, including GRM1, PRKCH, GNA12, GNA14, and CAMK2B. GRM1 encodes a metabotropic, G protein-coupled receptor for glutamate, the major excitatory neurotransmitters in the central nervous system [Hermans and Challiss, 2001]. A total of 10 SNPs from GRM1 were identified as top contributors to the component in our analysis. Nine out of these 10 SNPs contributed positively (see Supporting Information Table S2 for details). Note that the SNP-sMRI correlation was negative, indicating that subjects with higher loads of the SNP component carried relatively lower loads of the sMRI component. Also, the highlighted brain regions presented positive component weights, indicating that subjects with higher loads of the sMRI component had higher regional GMD. Thus, for these SNPs presenting positive component weights, our finding suggested that, overall, subjects carrying more minor alleles at these loci showed reduced GMD in the highlighted brain regions. Only one out of the 10 top SNPs in GRM1 (rs854144_T) contributed with a negative weight, indicating more minor alleles found in subjects exhibiting increased regional GMD. PRKCH is another molecule of great importance as it encodes a PKC subtype. PKCs phosphorylate a wide range of protein targets involved in brain functioning. For instance, phosphorylation of GluR1 by PKC is demonstrated to be critical for LTP expression [Boehm et al., 2006]. In our analysis, one single SNP rs1957902_G was identified from PRKCH, presenting a negative weight. GNA12 and GNA14 fall into the G-protein subfamilies of G₁₂ and G_q. GNA12 has been indicated to negatively regulate cell adhesion through interacting with cadherin [Meigs et al., 2002]. GNA14 is among the genes which were found to exhibit developmental expression variations in the cortex of a marmoset animal model, and linked to axonal guidance [Sasaki et al., in press]. In the present study, rs2258960_T and rs2644311_T were identified from GNA12 and presented positive weights. For GNA14, rs10869927_G and rs2889774_T showed negative weights while rs10781441_T, rs12684903_A, and rs7047853_G showed positive weights. CAMK2B encodes a Ca²⁺/calmodulin-dependent protein kinase involved in calcium signaling. The expression of this gene has been found to be disrupted in Alzheimer's disease [Antonell et al., 2013; Liang et al., 2008]. Three SNPs, rs12702075_A, rs10281178_C, and rs3934888_G, were identified from CAMK2B, all exhibiting negative weights.

Both the imaging and genetic findings from our analysis link strongly to neural development, indicating that the observed GMD variations might be traced back to developmental processes under control of genetic factors. It is well acknowledged that genetic components underlie anatomic variations in healthy and diseased human brains [Andersen, 2003; Giedd, 2005; Sotelo, 2004]. Direct associations between genetic variants and quantitative structural measures have also been demonstrated in large-scale studies [Bis et al., 2012; Stein et al., 2012]. Compared with previous works using univariate approaches, our results delineate relationships between particular genetic pathways and regional structural variations, emphasizing that these G-protein-related PKA, CREB, and nNOS mechanisms play a critical role in brain development which might ultimately lead to GMD differences. On the other hand, it should be noted that brain structure is the final manifestation of a complex interplay between multiple genetic and environmental factors. Our analysis captured a part of the story.

The current study benefited from a large homogeneous discovery sample which enhances statistical power. In addition, the employed multivariate approach assessed a group of variables whose aggregate effects would be more prominent compared to those of individual variables. Leveraging prior knowledge further improved the chance of pinpointing factors of interest in a large complex dataset. Most importantly, the imaging genetic association identified from the discovery sample was replicated in an independent cohort, indicating a low possibility of false positive results. On the other hand, one major limitation of this study lies in the different scanning platforms employed for imaging data collection, which can be a potential confound. To address this issue, we carefully explored all the scanning settings and eliminated from the imaging data the variance most likely induced by scanning discrepancies based on a linear model. Nonlinear scanning effects might still exist for which we do not have any knowledge at present. However, such effects were not expected to significantly contribute to the identified imaging genetic association given the linear decomposition of ICA. Another limitation is that it is not clear yet how the identified components are related to behavior based on the available data. The identified local GMD variations have been implicated in intelligence studies; however we did not collect IQ data in this project. Instead we investigated whether this imaging trait was correlated with the available reversal learning scores and found no significant associations. Further work needs to be done to better understand how the genetic and GMD variations might lead to differences in behavior. Besides, it should be noted that SNPs in high LD would exhibit comparable effects in our analysis. Therefore, SNPs might be identified due to tagging true causal variants. However, the identified genes and pathways should be unchanged. Finally, following the design of pICA-R, we only tested the largest cluster as a genetic reference for each candidate gene in this work, ignoring other smaller clusters which might also be biologically informative. This could be tackled with an extended approach, parallel ICA with multiple references [Chen et al., 2014a], which explores potential convergence of functional influences among genes. We plan to conduct such an analysis in a future study.

In summary, we performed a guided exploration in the present study, where a reference derived from the GNA14 gene managed to elicit a genetic component significantly associated with an sMRI component in a semi-blind multivariate analysis. The associated genetic component highlighted several neural signaling pathways which appear to interact with each other and are active in synaptic plasticity and axonal guidance. Meanwhile, the identified brain network comprised regions recruited in various cognitive processes and robustly implicated in brain maturation and intelligence. Collectively, our study suggests a key role of G-proteins in the genetic architecture underlying normal GMD variations in frontal and parietal regions. We speculate that the observed GMD variations partially result from differential genetic modulation of brain development, though future longitudinal studies are needed to dissect genetic contribution to trajectories of anatomic changes in developing brains.