Anita Lakatos

Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association Study Identifying Novel Susceptibility Genes for Alzheimer's Disease

Background With the exception of APOE ε4 allele, the common genetic risk factors for sporadic Alzheimers Disease (AD) are unknown. Methods and Findings We completed a genome-wide association study on 381 participants in the ADNI (Alzheimers Disease Neuroimaging Initiative) study. Samples were genotyped using the Illumina Human610-Quad BeadChip. 516,645 unique Single Nucleotide Polymorphisms (SNPs) were included in the analysis following quality control measures. The genotype data and raw genetic data are freely available for download (LONI, http://www.loni.ucla.edu/ADNI/Data/). Two analyses were completed: a standard case-control analysis, and a novel approach using hippocampal atrophy measured on MRI as an objectively defined, quantitative phenotype. A General Linear Model was applied to identify SNPs for which there was an interaction between the genotype and diagnosis on the quantitative trait. The case-control analysis identified APOE and a new risk gene, TOMM40 (translocase of outer mitochondrial membrane 40), at a genome-wide significance level of≤10−6 (10−11 for a haplotype). TOMM40 risk alleles were approximately twice as frequent in AD subjects as controls. The quantitative trait analysis identified 21 genes or chromosomal areas with at least one SNP with a p-value≤10−6, which can be considered potential “new” candidate loci to explore in the etiology of sporadic AD. These candidates included EFNA5, CAND1, MAGI2, ARSB, and PRUNE2, genes involved in the regulation of protein degradation, apoptosis, neuronal loss and neurodevelopment. Thus, we identified common genetic variants associated with the increased risk of developing AD in the ADNI cohort, and present publicly available genome-wide data. Supportive evidence based on case-control studies and biological plausibility by gene annotation is provided. Currently no available sample with both imaging and genetic data is available for replication. Conclusions Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimers disease that merit further investigation.

A Genome-Wide Association Study of Schizophrenia Using Brain Activation as a Quantitative Phenotype

BACKGROUND Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia. METHODS Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items. RESULTS Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress. CONCLUSION Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: Methodological considerations

Introduction. Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences. Methods. The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10–30%) and variations in allelic frequency for a Quantitative Trait (QT) (10–20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response. Conclusion. The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.

Identifying Gene Regulatory Networks in Schizophrenia

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.

The adaptive immune system restrains Alzheimer’s disease pathogenesis by modulating microglial function

Significance Neuroinflammation and activation of innate immunity are pathological hallmarks of Alzheimer’s disease (AD). In contrast, very few studies have examined the impact of the adaptive immune system in AD pathogenesis. Here, we find that genetic ablation of peripheral immune cell populations significantly accelerates amyloid pathogenesis, worsens neuroinflammation, and alters microglial activation state. Critically, it appears that loss of IgG-producing B cells impairs microglial phagocytosis, thereby exacerbating amyloid deposition. Conversely, replacement of IgGs via direct injection or bone marrow transplantation reverses these effects and reduces Aβ pathology. Together, these results highlight the importance of the adaptive immune system and its interactions with microglia in the pathogenesis of AD. The innate immune system is strongly implicated in the pathogenesis of Alzheimer’s disease (AD). In contrast, the role of adaptive immunity in AD remains largely unknown. However, numerous clinical trials are testing vaccination strategies for AD, suggesting that T and B cells play a pivotal role in this disease. To test the hypothesis that adaptive immunity influences AD pathogenesis, we generated an immune-deficient AD mouse model that lacks T, B, and natural killer (NK) cells. The resulting “Rag-5xfAD” mice exhibit a greater than twofold increase in β-amyloid (Aβ) pathology. Gene expression analysis of the brain implicates altered innate and adaptive immune pathways, including changes in cytokine/chemokine signaling and decreased Ig-mediated processes. Neuroinflammation is also greatly exacerbated in Rag-5xfAD mice as indicated by a shift in microglial phenotype, increased cytokine production, and reduced phagocytic capacity. In contrast, immune-intact 5xfAD mice exhibit elevated levels of nonamyloid reactive IgGs in association with microglia, and treatment of Rag-5xfAD mice or microglial cells with preimmune IgG enhances Aβ clearance. Last, we performed bone marrow transplantation studies in Rag-5xfAD mice, revealing that replacement of these missing adaptive immune populations can dramatically reduce AD pathology. Taken together, these data strongly suggest that adaptive immune cell populations play an important role in restraining AD pathology. In contrast, depletion of B cells and their appropriate activation by T cells leads to a loss of adaptive–innate immunity cross talk and accelerated disease progression.

Association between mitochondrial DNA variations and Alzheimer's Disease in the ADNI cohort

Despite the central role of amyloid deposition in the development of Alzheimers disease (AD), the pathogenesis of AD still remains elusive at the molecular level. Increasing evidence suggests that compromised mitochondrial function contributes to the aging process and thus may increase the risk of AD. Dysfunctional mitochondria contribute to reactive oxygen species (ROS) which can lead to extensive macromolecule oxidative damage and the progression of amyloid pathology. Oxidative stress and amyloid toxicity leave neurons chemically vulnerable. Because the brain relies on aerobic metabolism, it is apparent that mitochondria are critical for the cerebral function. Mitochondrial DNA sequence changes could shift cell dynamics and facilitate neuronal vulnerability. Therefore we postulated that mitochondrial DNA sequence polymorphisms may increase the risk of AD. We evaluated the role of mitochondrial haplogroups derived from 138 mitochondrial polymorphisms in 358 Caucasian Alzheimers Disease Neuroimaging Initiative (ADNI) subjects. Our results indicate that the mitochondrial haplogroup UK may confer genetic susceptibility to AD independently of the apolipoprotein E4 (APOE4) allele.

Increased CNV-Region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample

We investigated the genome-wide distribution of CNVs in the Alzheimers disease (AD) Neuroimaging Initiative (ADNI) sample (146 with AD, 313 with Mild Cognitive Impairment (MCI), and 181 controls). Comparison of single CNVs between cases (MCI and AD) and controls shows overrepresentation of large heterozygous deletions in cases (p-value<0.0001). The analysis of CNV-Regions identifies 44 copy number variable loci of heterozygous deletions, with more CNV-Regions among affected than controls (p=0.005). Seven of the 44 CNV-Regions are nominally significant for association with cognitive impairment. We validated and confirmed our main findings with genome re-sequencing of selected patients and controls. The functional pathway analysis of the genes putatively affected by deletions of CNV-Regions reveals enrichment of genes implicated in axonal guidance, cell-cell adhesion, neuronal morphogenesis and differentiation. Our findings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment.

Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample ☆ ☆☆

Careful selection of the reference region for non-quantitative positron emission tomography (PET) analyses is critically important for Region of Interest (ROI) data analyses. We introduce an empirical method of deriving the most suitable reference region for computing neurodegeneration sensitive (18)fluorodeoxyglucose (FDG) PET ratios based on the dataset collected by the Alzheimers Disease Neuroimaging Initiative (ADNI) study. Candidate reference regions are selected based on a heat map of the difference in coefficients of variation (COVs) of FDG ratios over time for each of the Automatic Anatomical Labeling (AAL) atlas regions normalized by all other AAL regions. Visual inspection of the heat map suggests that the portion of the cerebellum and vermis superior to the horizontal fissure is the most sensitive reference region. Analyses of FDG ratio data show increases in significance on the order of ten-fold when using the superior portion of the cerebellum as compared with the traditionally used full cerebellum. The approach to reference region selection in this paper can be generalized to other radiopharmaceuticals and radioligands as well as to other disorders where brain changes over time are hypothesized and longitudinal data is available. Based on the empirical evidence presented in this study, we demonstrate the usefulness of the COV heat map method and conclude that intensity normalization based on the superior portion of the cerebellum may be most sensitive to measuring change when performing longitudinal analyses of FDG-PET ratios as well as group comparisons in Alzheimers disease. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Association Between Mitochondrial DNA Haplogroup Variation and Autism Spectrum Disorders

Importance Autism spectrum disorders (ASD) are characterized by impairments in social interaction, communication, and repetitive or restrictive behavior. Although multiple physiologic and biochemical studies have reported defects in mitochondrial oxidative phosphorylation in patients with ASD, the role of mitochondrial DNA (mtDNA) variation has remained relatively unexplored. Objective To assess what impact mitochondrial lineages encompassing ancient mtDNA functional polymorphisms, termed haplogroups, have on ASD risk. Design, Setting, and Participants In this cohort study, individuals with autism and their families were studied using the Autism Genetic Resource Exchange cohort genome-wide association studies data previously generated at the Children’s Hospital of Philadelphia. From October 2010 to January 2017, we analyzed the data and used the mtDNA single-nucleotide polymorphisms interrogated by the Illumina HumanHap 550 chip to determine the mtDNA haplogroups of the individuals. Taking into account the familial structure of the Autism Genetic Resource Exchange data, we then determined whether the mtDNA haplogroups correlate with ASD risk. Main Outcomes and Measures Odds ratios of mitochondrial haplogroup as predictors of ASD risk. Results Of 1624 patients with autism included in this study, 1299 were boys (80%) and 325 were girls (20%). Families in the Autism Genetic Resource Exchange collection (933 families, encompassing 4041 individuals: 1624 patients with ASD and 2417 healthy parents and siblings) had been previously recruited in the United States with no restrictions on age, sex, race/ethnicity, or socioeconomic status. Relative to the most common European haplogroup HHV, European haplogroups I, J, K, O-X, T, and U were associated with increased risk of ASD, as were Asian and Native American haplogroups A and M, with odds ratios ranging from 1.55 (95% CI, 1.16-2.06) to 2.18 (95% CI, 1.59-3) (adjusted P < .04). Hence, mtDNA haplogroup variation is an important risk factor for ASD. Conclusions and Relevance Because haplogroups I, J, K, O-X, T, and U encompass 55% of the European population, mtDNA lineages must make a significant contribution to overall ASD risk.