431 research outputs found
DOCK4 and CEACAM21 as novel schizophrenia candidate genes in the Jewish population.
It is well accepted that schizophrenia has a strong genetic component. Several genome-wide association studies (GWASs) of schizophrenia have been published in recent years ; most of them population based with a case-control design. Nevertheless, identifying the specific genetic variants which contribute to susceptibility to the disorder remains a challenging task. A family-based GWAS strategy may be helpful in the identification of schizophrenia susceptibility genes since it is protected against population stratifi- cation, enables better accounting for genotyping errors and is more sensitive for identification of rare variants which have a very low frequency in the general population. In this project we implemented a family-based GWAS of schizophrenia in a sample of 107 Jewish-Israeli families. We found one genome- wide significant association in the intron of the DOCK4 gene (rs2074127, p value=1.134r10 x 7 ) and six additional nominally significant association signals with p<1r10 x 5 . One of the top single nucleotide polymorphisms (p<1r10 x 5 ) which is located in the predicted intron of the CEACAM21 gene was significantly replicated in independent family-based sample of Arab-Israeli origin (rs4803480 : p value=0.002 ; combined p value=9.61r10x8), surviving correction for multiple testing. Both DOCK4 and CEACAM21 are biologically reasonable candidate genes for schizophrenia although generalizability of the association of DOCK4 with schizophrenia should be investigated in further studies. In addition, gene-wide significant associations were found within three schizophrenia candidate genes : PGBD1, RELN and PRODH, replicating previously reported associations. By application of a family-based strategy to GWAS, our study revealed new schizophrenia susceptibility loci in the Jewish-Israeli popu- lation. Received 8 March 2011 ; Reviewed 11 April 2011 ; Revised 19 April 2011 ; Accepted 13 May 201
Bioarchaeological and palaeogenomic portrait of two Pompeians that died during the eruption of Vesuvius in 79 AD
The archaeological site of Pompeii is one of the 54 UNESCO World Heritage sites in Italy, thanks to its
uniqueness: the town was completely destroyed and buried by a Vesuviusā eruption in 79 AD. In this
work, we present a multidisciplinary approach with bioarchaeological and palaeogenomic analyses
of two Pompeian human remains from the Casa del Fabbro. We have been able to characterize the
genetic profle of the frst Pompeianā genome, which has strong afnities with the surrounding
central Italian population from the Roman Imperial Age. Our fndings suggest that, despite the
extensive connection between Rome and other Mediterranean populations, a noticeable degree
of genetic homogeneity exists in the Italian peninsula at that time. Moreover, palaeopathological
analyses identifed the presence of spinal tuberculosis and we further investigated the presence of
ancient DNA from Mycobacterium tuberculosis. In conclusion, our study demonstrates the power of
a combined approach to investigate ancient humans and confrms the possibility to retrieve ancient
DNA from Pompeii human remains. Our initial fndings provide a foundation to promote an intensive
and extensive paleogenetic analysis in order to reconstruct the genetic history of population from
Pompeii, a unique archaeological site
SNP-based pathway enrichment analysis for genome-wide association studies
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>SNP Set Enrichment Analysis </it>(SSEA), which contains a user-friendly interface and is freely available at <url>http://cbcl.ics.uci.edu/SSEA.</url></p> <p>Conclusions</p> <p>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.</p
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Metabolic correlates of prevalent mild cognitive impairment and Alzheimer's disease in adults with Down syndrome.
IntroductionDisruption of metabolic function is a recognized feature of late onset Alzheimer's disease (LOAD). We sought to determine whether similar metabolic pathways are implicated in adults with Down syndrome (DS) who have increased risk for Alzheimer's disease (AD).MethodsWe examined peripheral blood from 292 participants with DS who completed baseline assessments in the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) using untargeted mass spectrometry (MS). Our sample included 38 individuals who met consensus criteria for AD (DS-AD), 43 who met criteria for mild cognitive impairment (DS-MCI), and 211 who were cognitively unaffected and stable (CS).ResultsWe measured relative abundance of 8,805 features using MS and 180 putative metabolites were differentially expressed (DE) among the groups at false discovery rate-corrected q< 0.05. From the DE features, a nine-feature classifier model classified the CS and DS-AD groups with receiver operating characteristic area under the curve (ROC AUC) of 0.86 and a two-feature model classified the DS-MCI and DS-AD groups with ROC AUC of 0.88. Metabolite set enrichment analysis across the three groups suggested alterations in fatty acid and carbohydrate metabolism.DiscussionOur results reveal metabolic alterations in DS-AD that are similar to those seen in LOAD. The pattern of results in this cross-sectional DS cohort suggests a dynamic time course of metabolic dysregulation which evolves with clinical progression from non-demented, to MCI, to AD. Metabolomic markers may be useful for staging progression of DS-AD
Pharmacogenetics of autoimmune diseases: Research issues in the case of Multiple Sclerosis and the role of IFN-beta
Pharmacogenetics of auto-immune diseases is a complex field of application for this relatively new discipline, since we still have a partial knowledge of the biological mechanisms of the disease and of the drugs currently used to treat it. We address a few key issues that emerge when planning a pharmacogenetic investigation in Multiple Sclerosis and that relate to the complexities existing at the biological-genetic level and at the phenotypic characterization. In fact, we think that a clearer characterization of the clinical phenotype representing the end-point of the investigation together with a critical appraisal of the multi-faceted dimension of the genetic component of either the disease and the pharmacogenetic profile of the drug investigated, will help to design more thorough study and to achieve deeper understanding of the practical results. We will primarily focus our research considerations on the role of Interferon Beta (IFN-beta) as a prototypal therapeutic agent in Multiple Sclerosis
SNPLims: a data management system for genome wide association studies
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>Conclusions</p> <p>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 ācandidateā for the disease under consideration.</p
Increased CNV-Region Deletions in Mild Cognitive Impairment (MCI) and Alzheimer\u27s Disease (AD) Subjects in the ADNI Sample
We investigated the genome-wide distribution of CNVs in the Alzheimer\u27s disease (AD) Neuroimaging Initia- tive (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 hetero- zygous deletions in cases (p-value b 0.0001). The analysis of CNV-Regions identiļ¬es 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 signiļ¬cant for association with cognitive impairment. We validated and con- ļ¬rmed our main ļ¬ndings 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, cellācell adhesion, neuronal morphogenesis and differentiation. Our ļ¬ndings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment
The evolutionary history of common genetic variants influencing human cortical surface area
Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000ā3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure
Genomic profiling by whole-genome single nucleotide polymorphism arrays in Wilms tumor and association with relapse
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