Mapping heritability and molecular genetic associations with cortical features using probabilistic brain atlases: methods and applications to schizophrenia

Neuroimaging Genetics and Network Analysis in Alzheimer's Disease

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.

Twin studies of brain structure and cognition in schizophrenia

Twin studies in schizophrenia have been crucial in establishing estimates for the heritability and thus providing evidence for a genetic component in this disorder. Recent years have seen the application of the twin study paradigm to both putative intermediate phenotypes and biomarkers of disease as well as a diversification of its use in schizophrenia research. This review addressed studies of brain structure (T1 morphometry) and cognition in schizophrenia using twin study designs. We review major findings such as the overlap of genetic variance between schizophrenia and cognition as a model for the emergence of psychopathology. The use of novel hybrid models integrating molecular genetic risk markers, as well as the use of twin studies in epigenetics might prove to significantly enhance schizophrenia research in the post-GWAS era.

Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment

OBJECTIVE

This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

METHODS

Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

RESULTS

We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

CONCLUSION

We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

Structural Neuroimaging Genetics Interactions in Alzheimer's Disease

This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.

COMT, neuropsychological function and brain structure in schizophrenia: a systematic review and neurobiological interpretation

BACKGROUND

Endophenotypes in genetic psychiatry may increase our understanding of the molecular mechanisms underlying disease risk and its manifestations. We sought to investigate the link between neuropsychological impairments and brain structural abnormalities associated with the COMT Val(158)Met polymorphism in patients with schizophrenia to improve understanding of the pathophysiology of this disorder.

METHODS

We performed a systematic review using studies identified in PubMed and MEDLINE (from the date of the first available article to July 2012). Our review examined evidence of an association between the COMT Val(158)Met polymorphism and both neuropsychological performance and brain structure in patients with psychosis, in their relatives and in healthy individuals (step 1). The review also explored whether the neuropsychological tasks and brain structures identified in step 1 met the criteria for an endophenotype (step 2). Then we evaluated evidence that the neuropsychological endophenotypes identified in step 2 are associated with the brain structure endophenotypes identified in that step (step 3). Finally, we propose a neurobiological interpretation for this evidence.

RESULTS

A poorer performance on the n-back task and the Continuous Performance Test (CPT) and smaller temporal and frontal brain areas were associated with the COMT Val allele in patients with schizophrenia and their relatives and met most of the criteria for an endophenotype. It is possible that the COMT Val(158)Met polymorphism therefore contributes to the development of these neuropsychological and brain structural endophenotypes of schizophrenia, in which the prefrontal cortex may represent the neural substrate underlying both n-back and CPT performances.

LIMITATIONS

The association between a single genetic variant and an endophenotype does not necessarily imply a causal relationship between them.

CONCLUSION

This evidence and the proposed interpretation contribute to explain, at least in part, the biological substrate of 4 important endophenotypes that characterize schizophrenia.

The DTNBP1 (dysbindin-1) gene variant rs2619522 is associated with variation of hippocampal and prefrontal grey matter volumes in humans

DTNBP1 is one of the most established susceptibility genes for schizophrenia, and hippocampal volume reduction is one of the major neuropathological findings in this severe disorder. Consistent with these findings, the encoded protein dysbindin-1 has been shown to be diminished in glutamatergic hippocampal neurons in schizophrenic patients. The aim of this study was to investigate the effects of two single nucleotide polymorphisms of DTNBP1 on grey matter volumes in human subjects using voxel-based morphometry. Seventy-two subjects were included and genotyped with respect to two single nucleotide polymorphisms of DTNBP1 (rs2619522 and rs1018381). All participants underwent structural magnetic resonance imaging (MRI). MRI data were preprocessed and statistically analysed using standard procedures as implemented in SPM5 (Statistical Parametric Mapping), in particular the voxel-based morphometry (VBM) toolbox. We found significant effects of the DTNBP1 SNP rs2619522 bilaterally in the hippocampus as well as in the anterior middle frontal gyrus and the intraparietal cortex. Carriers of the G allele showed significantly higher grey matter volumes in these brain regions than T/T homozygotes. Compatible with previous findings on a role of dysbindin in hippocampal functions as well as in major psychoses, the present study provides first direct in vivo evidence that the DTNBP1 SNP rs2619522 is associated with variation of grey matter volumes bilaterally in the hippocampus.

Prefrontal deviations in function but not volume are putative endophenotypes for schizophrenia

This study sought to systematically investigate whether prefrontal cortex grey matter volume reductions are valid endophenotypes for schizophrenia, specifically investigating their presence in unaffected relatives, heritability, genetic overlap with the disorder itself and finally to contrast their performance on these criteria with putative neuropsychological indices of prefrontal functioning. We used a combined twin and family design and examined four prefrontal cortical regions of interest. Superior and inferior regions were significantly smaller in patients. However, the volumes of these same regions were normal in unaffected relatives and therefore, we could confirm that such deficits were not due to familial effects. Volumes of the prefrontal and orbital cortices were, however, moderately heritable, but neither shared a genetic overlap with schizophrenia. Total prefrontal cortical volume reductions shared a significant unique environmental overlap with the disorder, suggesting that the reductions were not familial. In contrast, prefrontal (executive) functioning deficits were present in the unaffected relatives, were moderately heritable and shared a substantial genetic overlap with liability to schizophrenia. These results suggest that the well recognized prefrontal volume reductions are not related to the same familial influences that increase schizophrenia liability and instead may be attributable to illness related biological changes or indeed confounded by illness trajectory, chronicity, medication or substance abuse, or in fact a combination of some or all of them.

Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence

The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.

Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort

A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred forty-two measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10(-7) and p<10(-6)). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.

Heritability estimates for cognitive factors and brain white matter integrity as markers of schizophrenia

Recent genetics research focusing on schizophrenia has led to candidate cognitive and neuroimaging variables as intermediate phenotypes or "endophenotype" markers for the illness. Among other stringent criteria, to be an endophenotype, a marker must demonstrate heritability. In an effort to explore the validity of a selection of cognitive and neuroimaging endophenotypes, the present study was designed to determine estimates of their heritability. One hundred fourteen subjects, including 27 with schizophrenia and 39 unaffected relatives from 23 multiplex schizophrenia families, participated in a comprehensive neuropsychological test battery and structural brain imaging with diffusion tensor imaging (DTI). Variables were selected if they previously have been demonstrated to show differences between people with schizophrenia and normal controls. Significant evidence of heritability was confirmed for overall cognitive function ("g"), as well as expressive and receptive language, verbal and visual memory, processing speed and cognitive inhibition. In addition, significant heritability estimates were determined for specific regions in the frontal, central, parietal, and occipital areas. These results suggest that the variables chosen may be useful endophenotypes for genetic and early detection studies, although further work with larger cohorts should be conducted to show that deficits in these functions and structures also segregate with schizophrenia within families and thus fully satisfy the definition of an endophenotype. In addition, other cognitive and neuroimaging variables that were not studied here may be candidates for schizophrenia endophenotypes.

Mapping gray matter development: implications for typical development and vulnerability to psychopathology

Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.

Aneuploidy: from a physiological mechanism of variance to Down syndrome

Quantitative differences in gene expression emerge as a significant source of variation in natural populations, representing an important substrate for evolution and accounting for a considerable fraction of phenotypic diversity. However, perturbation of gene expression is also the main factor in determining the molecular pathogenesis of numerous aneuploid disorders. In this review, we focus on Down syndrome (DS) as the prototype of "genomic disorder" induced by copy number change. The understanding of the pathogenicity of the extra genomic material in trisomy 21 has accelerated in the last years due to the recent advances in genome sequencing, comparative genome analysis, functional genome exploration, and the use of model organisms. We present recent data on the role of genome-altering processes in the generation of diversity in DS neural phenotypes focusing on the impact of trisomy on brain structure and mental retardation and on biological pathways and cell types in target brain regions (including prefrontal cortex, hippocampus, cerebellum, and basal ganglia). We also review the potential that genetically engineered mouse models of DS bring into the understanding of the molecular biology of human learning disorders.

Structural cerebral variations as useful endophenotypes in schizophrenia: do they help construct "extended endophenotypes"?

Endophenotypes represent intermediate phenotypes on the putative causal pathway from the genotype to the phenotype. They offer a potentially valuable strategy to examine the molecular etiopathology of complex behavioral phenotypes such as schizophrenia. Neurocognitive and neurophysiological impairments that suggest functional impairments associated with schizophrenia have been proposed as endophenotypes. However, few studies have examined the structural variations in the brain that might underlie the functional impairments as useful endophenotypes for schizophrenia. Over the past three decades, there has been an impressive body of literature supporting brain structural alterations in schizophrenia. We critically reviewed the extant literature on the neuroanatomical variations in schizophrenia in this paper to evaluate their candidacy as endophenotypes and how useful they are in furthering the understanding of etiology and pathophysiology of schizophrenia. Brain morphometric measures meet many of the criteria set by different investigators, such as being robustly associated with schizophrenia, heritable, quantifiable, and present in unaffected family members more frequently than in the general population. We conclude that the brain morphometric alterations appear largely to meet the criteria for endophenotypes in psychotic disorders. Some caveats for the utility of endophenotypes are discussed. A proposal to combine more than one endophenotype ("extended endophenotype") is suggested. Further work is needed to examine how specific genes and their interactions with the environment may produce alterations in brain structure and function that accompany psychotic disorders.

Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings

BACKGROUND

Shared neuropathological characteristics of patients with schizophrenia and their siblings might represent intermediate phenotypes that could be used to investigate genetic susceptibility to the illness. We sought to discover previously unidentified gray matter volume differences in patients with schizophrenia and their siblings with optimized voxel-based morphometry.

METHODS

We studied 169 patients with schizophrenia, 213 of their unaffected siblings, and 212 healthy volunteers from the Clinical Brain Disorders Branch/National Institute of Mental Health Genetic Study of Schizophrenia with magnetic resonance imaging.

RESULTS

Patients with schizophrenia had significant regional gray matter decreases in the frontal, temporal, and parietal cortices compared with healthy volunteers. Their unaffected siblings tended to share gray matter decreases in the medial frontal, superior temporal, and insular cortices, but these decreases were not significant after correction for multiple comparisons, even when we looked at a subgroup of siblings with a past history of mood disorder. As an exploratory analysis, we estimated heritability with regions of interest from the VBM analysis as well as from the hippocampus. Hippocampal volume was significantly correlated within sibling-pairs.

CONCLUSIONS

Our findings confirm and extend previous voxel-based morphometry analyses in ill subjects with schizophrenia. Furthermore, these data argue that although siblings might share some regional gray matter decreases with their affected siblings, the pattern of regional differences might be a weak intermediate phenotype for schizophrenia.

Polymorphisms in the homeobox gene OTX2 may be a risk factor for bipolar disorder

We investigated the possible involvement of OTX2, a homeobox gene crucial for forebrain development, in the pathogenesis of schizophrenia and bipolar disorder. The disruption of this gene results in cortical malformations and causes serotonergic and dopaminergic cells in the midbrain to be expressed in aberrant locations. Resequencing of DNA from OTX2 exons and surrounding introns from 60 individuals (15 schizophrenia, 15 bipolar disorder, 15 depression, and 15 control) revealed two intronic polymorphisms, rs2277499 (C/T) and rs28757218 (G/T), but no other variations. The minor allele of rs2277499 (T) did not associate with clinical diagnosis. However, using a Taqman genotyping assay, we found the rs28757218 minor allele (T) in 30 out of 720 (4.2%) individuals with bipolar disorder but only in 6 out of 526 (1.1%) control individuals (odds ratio 3.5, 95% confidence interval 1.4-10.4, P = 0.003). On the other hand, the rs28757218 minor allele was only found in 6 out of 458 (1.3%) individuals with schizophrenia. All individuals with the rs28757218 polymorphism were heterozygous for the allele. Based on this positive case-control association finding, we conclude that variations in OTX2 might confer risk for the development of bipolar disorder.

Mapping Genetic Influences on Brain Shape using Multi-Atlas Fluid Image Alignment

In this pilot study, we developed a set of computer vision based surface segmentation and statistical shape analysis algorithms to study genetic influences on brain structure in a database of brain MRI scans of normal twins. A set of manually delineated 3D parametric surfaces, representing the lateral ventricles, was deformed, using a Navier-Stokes fluid image registration algorithm, onto all the images in the database. The geometric transformations thus obtained were used to propagate the segmentation labels to all the other images. 3D radial distance maps were derived to encode anatomical shape differences. The proportion of shape variance attributable to genetic factors, known as the heritability, was estimated from the shape models using a restricted maximum likelihood method to increase statistical power. Segmentation errors associated with projecting labels onto new images were greatly reduced through multi-atlas averaging. The resulting algorithms provide a convenient and sensitive tool to recover and analyze small intra-pair image differences, and will make it easier to detect genetic influences on brain structure.

3D mapping of mini-mental state examination performance in clinical and preclinical Alzheimer disease

The Mini-mental State Examination (MMSE) is a brief cognitive screening instrument frequently used to track Alzheimer disease (AD) progression. We investigated the structural neuroimaging correlates of MMSE performance in patients with clinical and preclinical AD. We analyzed structural magnetic resonance imaging data from 29 probable AD and 5 MCI patients who later converted to probable AD using an advanced 3D cortical mapping technique. MMSE scores were entered as covariates in a general linear model that predicted the gray matter density at each cortical surface point. The results were corrected for multiple comparisons by permutation testing. The global permutation-corrected significance for the maps linking gray matter loss and cognitive decline was P=0.005 for the left and P=0.012 for the right hemisphere. Strongest correlations between MMSE score and gray matter integrity were seen in the entorhinal, parahippocampal, precuneus, superior parietal, and subgenual cingulate/orbitofrontal cortices. Significant correlations were also seen bilaterally in the temporal, the middle frontal and the left angular and supramarginal gyri. As a global cognitive measure, MMSE depends on the integrity of widely distributed cortical areas in both brain hemispheres with left-sided predominance.

Towards multimodal atlases of the human brain

Atlases of the human brain have an important impact on neuroscience. The emergence of ever more sophisticated imaging techniques, brain mapping methods and analytical strategies has the potential to revolutionize the concept of the brain atlas. Atlases can now combine data describing multiple aspects of brain structure or function at different scales from different subjects, yielding a truly integrative and comprehensive description of this organ. These integrative approaches have provided significant impetus for the human brain mapping initiatives, and have important applications in health and disease.