Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease
ABSTRACT Late-onset Alzheimer disease (LOAD) is a common disorder with a substantial genetic component. We postulate that many disease susceptibility variants act by altering gene expression levels.
We measured messenger RNA (mRNA) expression levels of 12 LOAD candidate genes in the cerebella of 200 subjects with LOAD. Using the genotypes from our LOAD genome-wide association study for the cis-single nucleotide polymorphisms (SNPs) (n = 619) of these 12 LOAD candidate genes, we tested for associations with expression levels as endophenotypes. The strongest expression cis-SNP was tested for AD association in 7 independent case-control series (2,280 AD and 2,396 controls).
We identified 3 SNPs that associated significantly with IDE (insulin degrading enzyme) expression levels. A single copy of the minor allele for each significant SNP was associated with approximately twofold higher IDE expression levels. The most significant SNP, rs7910977, is 4.2 kb beyond the 3' end of IDE. The association observed with this SNP was significant even at the genome-wide level (p = 2.7 x 10(-8)). Furthermore, the minor allele of rs7910977 associated significantly (p = 0.0046) with reduced LOAD risk (OR = 0.81 with a 95% CI of 0.70-0.94), as expected biologically from its association with elevated IDE expression.
These results provide strong evidence that IDE is a late-onset Alzheimer disease (LOAD) gene with variants that modify risk of LOAD by influencing IDE expression. They also suggest that the use of expression levels as endophenotypes in genome-wide association studies may provide a powerful approach for the identification of disease susceptibility alleles.
Internal Medicine News 03/2011; 44(5):2-2. DOI:10.1016/S1097-8690(11)70222-0
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ABSTRACT: Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community. © 2014 Wiley Periodicals, Inc.American Journal of Medical Genetics Part B Neuropsychiatric Genetics 03/2014; 165(2). DOI:10.1002/ajmg.b.32221 · 3.27 Impact Factor
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ABSTRACT: Variation in the expression of genes with putative roles in wood development was associated with single-nucleotide polymorphisms (SNPs) using a population of loblolly pine (Pinus taeda L.) that included individuals from much of the native range. Association studies were performed using 3938 SNPs and expression data obtained using quantitative real-time polymerase chain reaction (PCR) (qRT-PCR) for 106 xylem development genes in 400 clonally replicated loblolly pine individuals. A general linear model (GLM) approach, which takes the underlying population structure into consideration, was used to discover significant associations. After adjustment for multiple testing using a false discovery rate correction, 88 statistically significant associations (Q<0.05) were observed for 80 SNPs with the expression data of 33 xylem development genes. Thirty SNPs caused nonsynonymous mutations, 18 resulted in synonymous mutations, 11 were in 3' untranslated regions (UTRs), 1 was in a 5' UTR and 20 were in introns. Using AraNet, we found that Arabidopsis genes with high similarity to the loblolly pine genes involved in 21 of the 88 statistically significant associations are connected in functional gene networks. Comparisons of gene expression values revealed that in most cases the average expression in plants homozygous for the rare SNP allele was lower than that of plants that were heterozygous or homozygous for the abundant allele. Although there are association studies of SNPs and expression profiles for humans, Arabidopsis and white spruce, to the best of our knowledge, this is the first example of such an association genetic study in pines. Functional validation of these associations will lead to a deeper understanding of the molecular basis of phenotypic differences in wood development among individuals in conifer populations.Tree Physiology 07/2013; 33(7):763-74. DOI:10.1093/treephys/tpt054 · 3.41 Impact Factor