Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.18/2209
Título: Strategies for genome-wide association analysis: gene-centric approach, network-based analysis and copy number variants: 1st Triennium Post Doc Fellowship report
Autor: Tavares Correia, Catarina Alexandra
Palavras-chave: Perturbações do Desenvolvimento Infantil e Saúde Mental
Genome-wide association studies
Data: Jan-2013
Editora: Instituto Nacional de Saúde Doutor Ricardo Jorge, IP
Resumo: Genome-wide association studies (GWAS) have successfully identified numerous genetic risk factors for many common phenotypes, such as diabetes, Crohn's disease or height (http://www.genome.gov/gwastudies/). However, the enthusiasm surrounding GWAS for many complex diseases was tempered by the observation that the risk variants identified conferred only a small increment in risk, thus explaining a very small fraction of the genetic variation that we expect to exist. The mystery of “missing heritability” of common traits is now a major problem in human genetics. Evidence from classical quantitative genetic analysis, argues that most of the heritability missing in complex diseases is rather hidden below the threshold for genome-wide significant associations, due to the small effect of common risk variants for complex diseases, which represents a challenge for their individual detection using conventional single-marker association analysis. Realizing these limitations, new strategies and statistical approaches are needed to increase the power of GWAS analysis, shifting the focus from individual markers to the study of the cumulative effect of multiple genes acting on the same biological process. Additionally, the unprecedented pace in which huge amounts of biological data from multiple sources are produced, urges the need of tools to integrate data from multiple studies and sources. Integrative analysis of GWAS and expression data with independent biological knowledge under a rational biological hypothesis has been shown to be effective in the identification of pathways involved in several diseases and discovery of better predictors than individual genes. To address this issue, one of the main objectives of the present project was to develop an integrative genomics approach combining association data from GWAS with system-level data from protein-protein interactions in an attempt to uncover small effect risk variants from within the statistical “noise” in GWAS, and thus provide additional insight into complex diseases. Genes involved in the same disease are expected to be functionally related, converging in similar biological processes. On the other hand, Protein-protein interaction networks (PPI) are one of the strongest indications of functional relationship between genes, with interacting proteins often sharing similar functions, participating in the same biological process and contributing to related phenotypes. Thus mapping of GWAS associated genes into PPI may reveal functionally coherent subnetworks, which we hypothesize distinguish potentially relevant genes from spurious findings. To pursue this project we took advantage of the considerable amount of genotyping data publicly available from several complex diseases. In addition, the team where I am is part of a large-scale international consortium for Autism (Autism Genome Project - AGP), for which I have been actively contributing to the analysis of a two-stage GWAS recently carried on a sample of over 3000 trios (10% of Portuguese origin) genotyped for 1 million SNPs. Taking advantage of our proposed approach and the wealth of genetic information not yet publicly available generated by the AGP, we specifically intended to identify and prioritize ASD risk genes hidden within GWAS 'statistical noise for experimental follow up. Besides common variants of small effect, recent evidence suggests that rare variants may have an underestimated role in the genetic basis of complex diseases and might be significantly contributing to their missing heritability. Particularly in psychiatric disorders, such as autism, rare copy number variations (CNVs) have garnered much attention, and several CNV Genome-wide analyses showed a higher global burden of rare genic copy number variants, mostly de novo, in autistic and schizophrenic patients. However establish the clinical significance of specific CNVs is difficult and largely remains to be addressed. Given the data on genome-wide Copy Number Variant (CNV) generated by the AGP on a sample of about 3000 autistic patients extensively characterized clinically and behaviorally, another goal of this project was to establish the relevance for ASD etiology of potentially pathogenic CNVs identified in a Portuguese population sample by the AGP whole genome CNV analysis. The results gathered so far results revealed that there is relevant disease biological information within the range of GWAS statistical noise, and demonstrated that integration with protein networks is an effective strategy to extract this information. The application of our proposed approach to autism suggested novel susceptibility genes that will be explored for causal variants and therapeutic targeting and reinforce the link between oxidative stress and ASD. In addition, we establish the clinical significance of rare CNVs, in particular we provided multilevel evidence for a role of the annexin 1 gene in the etiology of ASD.
Descrição: Fellowship: SFRH/BPD/ 64281/2009
URI: http://hdl.handle.net/10400.18/2209
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