scholarly journals Quantifying genetic effects on disease mediated by assayed gene expression levels

2019 ◽  
Author(s):  
Douglas W. Yao ◽  
Luke J. O’Connor ◽  
Alkes L. Price ◽  
Alexander Gusev
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Genetic Effects ◽  
Genetic Component ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Gene Sets ◽  
Wide Range ◽  
Gene Expression Levels

AbstractDisease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

scholarly journals Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Chromatin Loops ◽  
Coding Regions ◽  
Affect Gene Expression ◽  
Gene Expression Levels

Abstract Background Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. Methods To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. Results We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Conclusion Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

scholarly journals The Contribution of RNA Decay Quantitative Trait Loci to Inter-Individual Variation in Steady-State Gene Expression Levels

PLoS Genetics ◽  
2012 ◽  
Vol 8 (10) ◽  
pp. e1003000 ◽  
Author(s):  
Athma A. Pai ◽  
Carolyn E. Cain ◽  
Orna Mizrahi-Man ◽  
Sherryl De Leon ◽  
Noah Lewellen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Quantitative Trait Loci ◽  
Individual Variation ◽  
Quantitative Trait ◽  
Rna Decay ◽  
Expression Levels ◽  
Trait Loci ◽  
Gene Expression Levels

Identification of Aberrantly Regulated Genes through Comparison of Genome-Wide Expression Profiles and Karyotype in Hyperdiploid >50 Chromosomes Pediatric Acute Lymphoblastic Leukemia.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 420-420
Author(s):  
Christian Flotho ◽  
Susana C. Raimondi ◽  
James R. Downing
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Expression Profiles ◽  
Chromosome 21 ◽  
Small Subset ◽  
Specific Gene ◽  
Expression Levels ◽  
Number Of Genes ◽  
The Mean ◽  
Gene Expression Levels

Abstract We have demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-ALL, E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid >50 chromosomes (HD>50). Interestingly, almost 70% of the genes that defined HD>50 ALL localized to chromosome 21 or X. To further explore the relationship between gene expression and chromosome dosage, we compared the expression profiles obtained using the Affymetrix U133A&B microarrays of 17 HD>50 ALLs to 78 diploid or pseudodiploid ALLs. Our analysis demonstrated that the average expression level for all genes on a chromosome could be used to predict chromosome copy numbers. Specifically, the copy number for each chromosome calculated by gene expression profiling predicted the numerical chromosomal abnormalities detected by standard cytogenetics. For chromosomes that were trisomic in HD>50 ALL, the mean chromosome-specific gene expression level was increased approximately 1.5-fold compared to that observed in diploid or pseudodiploid ALL cases. Similarly, for chromosome 21 and X, the mean chromosome-specific gene expression levels were increased approximately 2-fold, consistent with a duplication of the active X chromosome and tetrasomy of chromosome 21, a finding verified by standard cytogenetics in >90% of the HD>50 cases. These finding indicate that the aberrant gene expression levels seen in HD>50 ALL primarily reflect gene dosages. Importantly, we did not observe any clustering of aberrantly expressed genes across the duplicated chromosomes, making regional gain or loss of genomic material unlikely. Paradoxically, however, a more detailed analysis revealed a small but statistically significant number of genes on the trisomic/tetrasomic chromosomes whose expression levels were markedly reduced when compared to that seen in diploid or pseudodiploid leukemic samples. Using the Statistical Analysis of Microarrays (SAM) algorithm we identified 20 genes whose expression was reduced >2-fold despite having an increase in copy number. Interestingly, included within this group are several known tumor suppressors, including AKAP12, which is specifically silenced by methylation in fos-transformed cells, and IGF2R and IGFBP7, negative regulators of insulin-like growth factor signaling. In addition to the silencing of a small subset of genes, we also identified 21 genes on these chromosomes whose expression levels were markedly higher (>3-fold) than would be predicted solely based on copy number. Although the mechanism responsible for their increased expression remains unknown, included in this group are four genes involved in signal transduction (IL3RA, IL13RA1, SNX9, and GASP) and a novel cytokine, C17, whose expression is normally limited to CD34+ hematopoietic progenitors. Taken together, these data suggest that aberrant growth in HD>50 ALL is in part driven by increased expression of a large number of genes secondary to chromosome duplications, coupled with a further enhanced expression of a limited number of growth promoting genes, and the specific silencing of a small subset of negative growth regulatory genes. Understanding the mechanisms responsible for the non-dosage related changes in gene expression should provide important insights into the pathology of HD>50 ALL.

scholarly journals Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease

Neurology ◽  
2010 ◽  
Vol 74 (6) ◽  
pp. 480-486 ◽  
Author(s):  
F. Zou ◽  
M. M. Carrasquillo ◽  
V. S. Pankratz ◽  
O. Belbin ◽  
K. Morgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer Disease ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Genome Wide ◽  
Gene Expression Levels

scholarly journals Estimating the proportion of disease heritability mediated by gene expression levels

2017 ◽  
Author(s):  
Luke J. O’Connor ◽  
Alexander Gusev ◽  
Xuanyao Liu ◽  
Po-Ru Loh ◽  
Hilary K. Finucane ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Disease Risk ◽  
Genetic Component ◽  
Effect Sizes ◽  
Expression Levels ◽  
Risk Variants ◽  
Regulatory Effects ◽  
Disease Effect ◽  
Gene Expression Levels

AbstractDisease risk variants identified by GWAS are predominantly noncoding, suggesting that gene regulation plays an important role. eQTL studies in unaffected individuals are often used to link disease-associated variants with the genes they regulate, relying on the hypothesis that noncoding regulatory effects are mediated by steady-state expression levels. To test this hypothesis, we developed a method to estimate the proportion of disease heritability mediated by the cis-genetic component of assayed gene expression levels. The method, gene expression co-score regression (GECS regression), relies on the idea that, for a gene whose expression level affects a phenotype, SNPs with similar effects on the expression of that gene will have similar phenotypic effects. In order to distinguish directional effects mediated by gene expression from non-directional pleiotropic or tagging effects, GECS regression operates on pairs of cis SNPs in linkage equilibrium, regressing pairwise products of disease effect sizes on products of cis-eQTL effect sizes. We verified that GECS regression produces robust estimates of mediated effects in simulations. We applied the method to eQTL data in 44 tissues from the GTEx consortium (average NeQTL = 158 samples) in conjunction with GWAS summary statistics for 30 diseases and complex traits (average NGWAS = 88K) with low pairwise genetic correlation, estimating the proportion of SNP-heritability mediated by the cis-genetic component of assayed gene expression in the union of the 44 tissues. The mean estimate was 0.21 (s.e. = 0.01) across 30 traits, with a significantly positive estimate (p < 0.001) for every trait. Thus, assayed gene expression in bulk tissues mediates a statistically significant but modest proportion of disease heritability, motivating the development of additional assays to capture regulatory effects and the use of our method to estimate how much disease heritability they mediate.

The development of the DCIS score: Scaling and normalization in the Marin General Hospital cohort.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 190-190 ◽  
Author(s):  
Frederick L. Baehner ◽  
Steven M Butler ◽  
Carl N. Yoshizawa ◽  
Che Prasad ◽  
Diana B. Cherbavaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
General Hospital ◽  
Risk Group ◽  
Recurrence Score ◽  
Low Risk ◽  
Tumor Characteristics ◽  
Clinicopathologic Characteristics ◽  
Expression Levels ◽  
Wide Range ◽  
Gene Expression Levels

190 Background: In selected low-risk patients with DCIS treated with wide local excision without radiation, the DCIS score was validated as a predictor of 10 year risk of an ipsilateral breast event (IBE - recurrence of in situ or invasive carcinoma) (p = 0.02) (Solin; SABCS 2011). As part of the development of the DCIS score, scaling from 0 to 100 and determination of risk group cutoff values was done using 100 patient DCIS samples from Marin General Hospital (MGH) selected to have a wide range of tumor characteristics. Methods: 100 patient specimens diagnosed with DCIS were provided by MGH. The Oncotype DX assay was performed, normalized expression levels for the 16 cancer related genes were determined, the DCIS Score and Recurrence Score (RS) were calculated. Distributions of the DCIS score, RS, and individual gene expression levels were described overall and by tumor characteristics. Treatment and pt outcome data were not available. Results: Samples for 96 pts had sufficient tumor and were evaluable; 47% had high nuclear grade, 52% comedo necrosis, 32% tumor size >10 mm, and 9% were ER-negative by IHC. After scaling and risk group cutoff determination, the DCIS score was low risk (0-38) for 49%, intermediate risk (39-54) for 27%, and high risk (≥55) for 24% of pts (Table). The DCIS score was widely distributed within subgroups defined by each of the clinical and pathology characteristics. Proliferation gene expression levels were low in DCIS, on average, relative to prior studies in invasive breast cancer. 92% had a proliferation gene group score <6.5, the threshold used when the RS is calculated; no threshold is used in calculating the DCIS score. Conclusions: Optimal scaling and risk cutoff determination for a wide range of all clinicopathologic characteristics provides for a wide distribution for the DCIS Score. [Table: see text]

scholarly journals Quantifying genetic effects on disease mediated by assayed gene expression levels

2020 ◽  
Vol 52 (6) ◽  
pp. 626-633 ◽  
Author(s):  
Douglas W. Yao ◽  
Luke J. O’Connor ◽  
Alkes L. Price ◽  
Alexander Gusev
Keyword(s):  
Gene Expression ◽  
Genetic Effects ◽  
Expression Levels ◽  
Gene Expression Levels
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