Gene dose influences cellular and calcium channel dysregulation in heterozygous and homozygous T4826I-RYR1 malignant hyperthermia-susceptible muscle

Malignant hyperthermia susceptibility (MHS) is primarily conferred by mutations within ryanodine receptor type 1 (RYR1). Here we address how the MHS mutation T4826I within the S4-S5 linker influences excitation-contraction coupling and resting myoplasmi

Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer.

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.We thank the two anonymous reviewers for their constructive comments. This research was supported by the National Institutes of Health (NIH) grant R01CA169122; P.W. was supported by NIH grants R01HL116720 and R21HL126032. S.H.O. was supported by NIH grant P30CA008748. R.E.N. and the Queensland Pancreatic Cancer Study were funded by the Australian National Health and Medical Research Council. The authors thank Ms. Jessica Swann and the National Institute of Statistical Sciences writing workshop for editorial assistance and suggestions. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing computing resources. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. The authors declare that there is no conflict of interest

Genetically inferred birthweight, height, and puberty timing and risk of osteosarcoma

INTRODUCTION: Several studies have linked increased risk of osteosarcoma with tall stature, high birthweight, and early puberty, although evidence is inconsistent. We used genetic risk scores (GRS) based on established genetic loci for these traits and evaluated associations between genetically inferred birthweight, height, and puberty timing with osteosarcoma. METHODS: Using genotype data from two genome-wide association studies, totaling 1039 cases and 2923 controls of European ancestry, association analyses were conducted using logistic regression for each study and meta-analyzed to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup analyses were conducted by case diagnosis age, metastasis status, tumor location, tumor histology, and presence of a known pathogenic variant in a cancer susceptibility gene. RESULTS: Genetically inferred higher birthweight was associated with an increased risk of osteosarcoma (OR =1.59, 95% CI 1.07-2.38, P = 0.02). This association was strongest in cases without metastatic disease (OR =2.46, 95% CI 1.44-4.19, P = 9.5 ×10-04). Although there was no overall association between osteosarcoma and genetically inferred taller stature (OR=1.06, 95% CI 0.96-1.17, P = 0.28), the GRS for taller stature was associated with an increased risk of osteosarcoma in 154 cases with a known pathogenic cancer susceptibility gene variant (OR=1.29, 95% CI 1.03-1.63, P = 0.03). There were no significant associations between the GRS for puberty timing and osteosarcoma. CONCLUSION: A genetic propensity to higher birthweight was associated with increased osteosarcoma risk, suggesting that shared genetic factors or biological pathways that affect birthweight may contribute to osteosarcoma pathogenesis

Genome-wide analyses identify KLF4 as an important negative regulator in T-cell acute lymphoblastic leukemia through directly inhibiting T-cell associated genes

é 2015 Li et al. Background: Kruppel-like factor 4 (KLF4) induces tumorigenesis or suppresses tumor growth in a tissue-dependent manner. However, the roles of KLF4 in hematological malignancies and the mechanisms of action are not fully understood. Methods: Inducible KLF4-overexpression Jurkat cell line combined with mouse models bearing cell-derived xenografts and primary T-cell acute lymphoblastic leukemia (T-ALL) cells from four patients were used to assess the functional role of KLF4 in T-ALL cells in vitro and in vivo. A genome-wide RNA-seq analysis was conducted to identify genes regulated by KLF4 in T-ALL cells. Chromatin immunoprecipitation (ChIP) PCR was used to determine direct binding sites of KLF4 in T-ALL cells. Results: Here we reveal that KLF4 induced apoptosis through the BCL2/BCLXL pathway in human T-ALL cell lines and primary T-ALL specimens. In consistence, mice engrafted with KLF4-overexpressing T-ALL cells exhibited prolonged survival. Interestingly, the KLF4-induced apoptosis in T-ALL cells was compromised in xenografts but the invasion capacity of KLF4-expressing T-ALL cells to hosts was dramatically dampened. We found that KLF4 overexpression inhibited T cell-associated genes including NOTCH1, BCL11B, GATA3, and TCF7. Further mechanistic studies revealed that KLF4 directly bound to the promoters of NOTCH1, BCL2, and CXCR4 and suppressed their expression. Additionally, KLF4 induced SUMOylation and degradation of BCL11B. Conclusions: These results suggest that KLF4 as a major transcription factor that suppresses the expression of T-cell associated genes, thus inhibiting T-ALL progression.Link_to_subscribed_fulltex

Rare and low-frequency exonic variants and gene-by-smoking interactions in pulmonary function

Genome-wide association studies have identified numerous common genetic variants associated with spirometric measures of pulmonary function, including forced expiratory volume in one second (FEV1), forced vital capacity, and their ratio. However, variants with lower minor allele frequencies are less explored. We conducted a large-scale gene-smoking interaction meta-analysis on exonic rare and low-frequency variants involving 44,429 individuals of European ancestry in the discovery stage and sought replication in the UK BiLEVE study with 45,133 European ancestry samples and UK Biobank study with 59,478 samples. We leveraged data on cigarette smoking, the major environmental risk factor for reduced lung function, by testing gene-by-smoking interaction effects only and simultaneously testing the genetic main effects and interaction effects. The most statistically significant signal that replicated was a previously reported low-frequency signal in GPR126, distinct from common variant associations in this gene. Although only nominal replication was obtained for a top rare variant signal rs142935352 in one of the two studies, interaction and joint tests for current smoking and PDE3B were s

Statistical Methods for Gene-Environment Interactions and High-Dimensional Mediation Analysis

As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called “aGE” tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. We show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. To further increase the power of GxE and improve the understanding of underlying molecular mechanisms, we have proposed to incorporate genome functional information to the proposed aGE test, which can address multiple sets of external weights, for example, derived from gene expressions in different tissues. We show that this test can control the type 1 error rate, and the power is resilient to the inclusion of non-causal variants and non-informative external weights. We demonstrate the performance of the proposed aGE and aGE weighted test using Pancreatic Cancer Case-Control Consortium data. Environmental exposures can regulate intermediate molecular phenotypes by different mechanisms and thus lead to different health outcomes. It is of significant scientific interest to explore the relationship between environmental exposure and traits beyond association and to unravel the role of potentially high-dimensional intermediate phenotypes. Mediation analysis is an important tool to investigate such relationship. However, there is a lack of a good overall measure of mediation effect, especially under the high-dimensional setting. Here we propose extending an R-squared (Rsq) effect size measure, originally proposed in the single-mediator setting, to the multiple and high-dimensional setting. We showed that our new measure outperforms several frequently-used mediation measures, including product, proportion and ratio measure in terms of bias and variance. To mitigate potential bias induced by non-mediators, we further examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate, to exclude non-mediators and we evaluate the consistency of the estimation procedures. Lastly, we applied our novel Rsq measure to quantify the amount of variation of systolic blood pressure and lung function explained by gene expression in the Framingham Heart Study and introduce a resampling-based confidence interval for this Rsq measure