Publications (9)40.39 Total impact
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Article: Role of UBE3A and ATP10A genes in autism susceptibility region 15q11-q13 in an Italian population: a positive replication for UBE3A.
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ABSTRACT: The aetiology of autism is still largely unknown despite analyses from family and twin studies demonstrating substantial genetic role in the aetiology of the disorder. Data from linkage studies and analyses of chromosomal abnormalities identified 15q11-q13 as a region of particular aetiopathogenesis interest. We screened a set of markers spanning two known imprinted, maternally expressed genes, UBE3A and ATP10A, harboured in this candidate region. We replicated evidence of linkage disequilibrium (LD) at marker D15S122, located at the 5' end of UBE3A and originally reported by Nurmi et al. (2001). The potential role of UBE3A in our family-based association study is further supported by the association of two haplotypes that include one of the alleles of D15S122 and by the transmission disequilibrium test (TDT) evidence of the same allele in a parent of origin effect analysis. In a secondary analysis, we provided the first evidence of a significant association between first word delay and psychomotor regression with the 15q11-q13 region. Our data support a potential role of UBE3A in the complex pathogenic mechanisms of autism.Psychiatry Research 01/2011; 185(1-2):33-8. · 2.52 Impact Factor -
Article: SNP-based pathway enrichment analysis for genome-wide association studies.
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ABSTRACT: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.BMC Bioinformatics 01/2011; 12:99. · 2.75 Impact Factor -
Article: Identifying gene regulatory networks in schizophrenia.
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ABSTRACT: 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.NeuroImage 11/2010; 53(3):839-47. · 5.89 Impact Factor -
Article: Association between mitochondrial DNA variations and Alzheimer's disease in the ADNI cohort.
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ABSTRACT: Despite the central role of amyloid deposition in the development of Alzheimer's 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 Alzheimer's 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.Neurobiology of aging 08/2010; 31(8):1355-63. · 5.94 Impact Factor -
Article: Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations.
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ABSTRACT: 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. 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. 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.Cognitive Neuropsychiatry 08/2009; 14(4-5):391-418. -
Article: Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease.
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ABSTRACT: With the exception of APOE epsilon4 allele, the common genetic risk factors for sporadic Alzheimer's Disease (AD) are unknown. We completed a genome-wide association study on 381 participants in the ADNI (Alzheimer's 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 < or =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 < or =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. Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimer's disease that merit further investigation.PLoS ONE 02/2009; 4(8):e6501. · 4.09 Impact Factor -
Article: A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype.
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ABSTRACT: 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. 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. 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. 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.Schizophrenia Bulletin 12/2008; 35(1):96-108. · 8.80 Impact Factor -
Article: SNPLims: a data management system for genome wide association studies.
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ABSTRACT: Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases. We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses. We have developed an information system mainly devoted to the storage and management of SNP genotype data produced by the Illumina platform from the raw outputs of genotyping into a relational database. The relational database can be accessed in order to import any existing data and export user-defined formats compatible with many different genetic analysis programs. After calculating family-based or case-control association study data, the results can be imported in SNPLims. One of the main features is to allow the user to rapidly identify and annotate statistically relevant polymorphisms from the large volume of data analyzed. Results can be easily visualized either graphically or creating ASCII comma separated format output files, which can be used as input to further analyses. The proposed infrastructure allows to manage a relatively large amount of genotypes for each sample and an arbitrary number of samples and phenotypes. Moreover, it enables the users to control the quality of the data and to perform the most common screening analyses and identify genes that become "candidate" for the disease under consideration.BMC Bioinformatics 02/2008; 9 Suppl 2:S13. · 2.75 Impact Factor -
Article: Common genetic variants and haplotypes in renal CLCNKA gene are associated to salt-sensitive hypertension.
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ABSTRACT: Abnormal renal reabsorption of sodium (Na(+)) is likely to play a role in the pathogenesis of salt-sensitivity. In the kidney, chloride channels CLC-Ka (gene CLCNKA) and CLC-Kb (gene CLCNKB) and their subunit Barttin (gene BSND) have important effects on the control of Na(+) and water homeostasis. We investigated if single nucleotide polymorphisms (SNPs) or haplotypes within CLCNKA, CLCNKB and BSND loci affect salt-sensitivity in hypertensive subjects. Associations between blood pressure (BP) change after Na(+)-load and 15 SNPs spanning the length of CLCNKA and CLCNKB and six SNPs spanning the length of BSND were studied in 314 never treated essential hypertensives who underwent an i.v. infusion of saline (300 mm NaCl in 2 l H(2)O in 120 min). Four SNPs were significantly associated with BP change after Na-load. Rs848307 (P = 0.0026) and rs1739843 (P = 0.0023) map upstream the 5' of CLCNKA. Non-coding Rs1010069 (P = 0.0006) and non-synonymous rs1805152 (Thr447Ala; P = 0.0078) map within CLCNKA. Moreover, basal plasma renin activity and heart rate (measured before Na-load) were significantly lower in patients carrying the alleles associated with the larger mean BP increase after Na-load, indicating that such alleles are associated with chronic volume expansion. This study supports the candidacy of CLCNKA as a new susceptibility gene for salt-sensitivity.Human Molecular Genetics 08/2007; 16(13):1630-8. · 7.64 Impact Factor
Top co-authors
Top Journals
- BMC Bioinformatics (2)
- Psychiatry Research (1)
- Schizophrenia Bulletin (1)
- Human Molecular Genetics (1)
- NeuroImage (1)
Institutions
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2011
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University of Milan
Milano, Lombardy, Italy
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2008–2009
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University of California, Irvine
- Department of Psychiatry & Human Behavior
Irvine, CA, USA
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