Most genetic risk for autism resides with common variation.
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ABSTRACT:
SUBMITTER: Gaugler T
PROVIDER: S-EPMC4137411 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Publications
Most genetic risk for autism resides with common variation.
Gaugler Trent T Klei Lambertus L Sanders Stephan J SJ Bodea Corneliu A CA Goldberg Arthur P AP Lee Ann B AB Mahajan Milind M Manaa Dina D Pawitan Yudi Y Reichert Jennifer J Ripke Stephan S Sandin Sven S Sklar Pamela P Svantesson Oscar O Reichenberg Abraham A Hultman Christina M CM Devlin Bernie B Roeder Kathryn K Buxbaum Joseph D JD
Nature genetics 20140720 8
A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from rare variation, especially de novo mutations. From this evidence, one might conclude that rare variation dominates the allelic spectrum in autism, yet recent studies show that common variation, individually of small effect, has substantial impa ...[more]
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Project description:BackgroundAutism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent deficits in social communication and interaction. Common genetic variation appears to play a key role in the development of this condition. In this systematic review, we describe the relationship between genetic variations and autism. We created a gene dataset of the genes involved in the pathogenesis of autism and performed an over-representation analysis to evaluate the biological functions and molecular pathways that may explain the associations between these variants and the development of ASD.Results177 studies and a gene set composed of 139 were included in this qualitative systematic review. Enriched pathways in the over-representation analysis using the KEGG pathway database were mostly associated with neurotransmitter receptors and their subunits. Major over-represented biological processes were social behavior, vocalization behavior, learning and memory. The enriched cellular component of the proteins encoded by the genes identified in this systematic review were the postsynaptic membrane and the cell junction.ConclusionsAmong the biological processes that were examined, genes involved in synaptic integrity, neurotransmitter metabolism, and cell adhesion molecules were significantly involved in the development of autism.
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Project description:Insulin-like growth factor (IGF) 1 and its binding proteins foster cellular proliferation and inhibit apoptosis. In vitro studies show that IGF1 increases ovarian cell growth and invasive potential, suggesting a role for the IGF1 pathway in ovarian cancer etiology. We evaluated genetic variation in the IGF1, IGFBP1 and IGFBP3 genes in relation to ovarian cancer risk by genotyping 29 haplotype-tagging single nucleotide polymorphisms in 1173 cases and 1201 controls from the New England Case-Control (NECC) study and 296 cases and 854 controls from the Nurses' Health Study (NHS). The association of haplotypes and single nucleotide polymorphisms (SNPs) with ovarian cancer was estimated using unconditional (NECC) and conditional (NHS) logistic regression. Additionally, we evaluated the association of SNPs with IGF1, IGF-binding protein (IGFBP) 3 and IGFBP2 plasma levels (n = 380 NHS controls). Our data suggest a decreased risk for women carrying haplotype 2C of the IGF1 gene [odds ratios (ORs) = 0.82, 95% confidence intervals (CIs) = 0.69-0.98] and an increased risk for women carrying haplotype 1D (OR = 1.41, 95% CI = 1.03-1.94) or 2D (OR = 1.20, 95% CI = 1.01-1.41) in the binding proteins. When evaluated individually, three SNPs in the IGFBPs (rs10228265, rs4988515 and rs2270628) were associated with increased ovarian cancer risk, and several IGF1 (rs11111285, rs1996656 and rs1019731) and IGFBP3 (rs2270628, rs2854746 and rs2854744) SNPs were significantly associated with IGF1, IGFBP3 and IGFBP2 plasma levels. Some haplotypes and SNPs in the IGF pathway genes may be associated with ovarian cancer risk; however, these results need to be confirmed. Of particular interest was the IGFBP3 SNP rs2270628, which was associated with both increased IGF1 plasma levels and higher ovarian cancer risk.
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Project description:BACKGROUND:Autism is a complex disorder characterized by deficits involving communication, social interaction, and repetitive and restrictive patterns of behavior. Twin studies have shown that autism is strongly heritable, suggesting a strong genetic component. In other disease states with a complex etiology, such as type 2 diabetes, cancer and cardiovascular disease, combined analysis of multiple genetic variants in a genetic score has helped to identify individuals at high risk of disease. Genetic scores are designed to test for association of genetic markers with disease. METHOD:The accumulation of multiple risk alleles markedly increases the risk of being affected, and compared with studying polymorphisms individually, it improves the identification of subgroups of individuals at greater risk. In the present study, we show that this approach can be applied to autism by specifically looking at a high-risk population of children who have siblings with autism. A two-sample study design and the generation of a genetic score using multiple independent genes were used to assess the risk of autism in a high-risk population. RESULTS:In both samples, odds ratios (ORs) increased significantly as a function of the number of risk alleles, with a genetic score of 8 being associated with an OR of 5.54 (95% confidence interval [CI] 2.45 to 12.49). The sensitivities and specificities for each genetic score were similar in both analyses, and the resultant area under the receiver operating characteristic curves were identical (0.59). CONCLUSIONS:These results suggest that the accumulation of multiple risk alleles in a genetic score is a useful strategy for assessing the risk of autism in siblings of affected individuals, and may be better than studying single polymorphisms for identifying subgroups of individuals with significantly greater risk.
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