scholarly journals Exploring the variance in complex traits captured by DNA methylation assays

2020 ◽  
Author(s):  
Thomas Battram ◽  
Tom R. Gaunt ◽  
Doug Speed ◽  
Nicholas J. Timpson ◽  
Gibran Hemani

ABSTRACTFollowing years of epigenome-wide association studies (EWAS), traits analysed to date tend to yield few associations. Reinforcing this observation, we conducted EWAS on 400 traits and 16 yielded at least one association at the conventional significance threshold (P<1×10−7). To investigate why EWAS yield is low, we formally estimated the proportion of phenotypic variation captured by 421,693 blood derived DNA methylation markers (h2EWAS) across all 400 traits. The mean h2EWAS was zero, with evidence for regular cigarette smoking exhibiting the largest association with all markers (h2EWAS=0.42) and the only one surpassing a false discovery rate < 0.1. Though underpowered to determine the h2EWAS value for any one trait, h2EWAS was predictive of the number of EWAS hits across the traits analysed (AUC=0.7). Modelling the contributions of the methylome on a per-site versus a per-region basis gave varied h2EWAS estimates (r=0.47) but neither approach obtained substantially higher model fits across all traits. Our analysis indicates that most complex traits do not heavily associate with markers commonly measured in EWAS within blood. However, it is likely DNA methylation does capture variation in some traits and h2EWAS may be a reasonable way to prioritise traits that are likely to yield associations.

2020 ◽  
Author(s):  
Anna Hutchinson ◽  
Guillermo Reales ◽  
Chris Wallace

1.AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary data has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate (FDR) can be a more powerful approach as sample sizes increase and many associations are expected in each study. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions. We relax the distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary data from any continuous distribution (“Flexible cFDR”). Our method is iterative, whereby additional layers of auxiliary data can be leveraged in turn. Through simulations we show that flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional data to find additional genetic associations for asthma, which we validated in the larger UK Biobank data resource.


2017 ◽  
Author(s):  
Rong W. Zablocki ◽  
Richard A. Levine ◽  
Andrew J. Schork ◽  
Shujing Xu ◽  
Yunpeng Wang ◽  
...  

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p ≤ 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr ≤ 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.


Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3277-3277 ◽  
Author(s):  
Aleah L. Smith ◽  
Joo Jungnam ◽  
Sheila Rao ◽  
Andreas Lundqvist ◽  
Lisa W. Cook ◽  
...  

Abstract Previous studies have shown that granulocyte colony stimulating factor (G-CSF) mobilization skews T-cells toward a type 2 cytokine profile, potentially impacting GVHD and other immune mediated events that occur after allogeneic hematopoietic stem cell transplantation (HCT). AMD3100, a selective antagonist of CXCR4, rapidly mobilizes hematopoietic progenitor cells into the circulation, has a synergistic effect on CD34+ cell mobilization when combined with G-CSF, and is currently being evaluated as a single agent to mobilize allografts. Apheresis collections mobilized with a single injection of AMD3100 contain a similar number of T-cells as those collected following 5 daily doses of G-CSF. We investigated whether T-cells mobilized with AMD3100 undergo changes in cytokine polarization status as described to occur with G-CSF mobilization. Using real time PCR, we investigated the expression of 84 genes associated with TH1, TH2, and TH3 T-cell pathways at baseline and following mobilization with a single injection of AMD3100 (dosed at 240 or 320 mcg/kg; n=12 subjects) or following 5 daily doses of G-CSF(n=5 subjects). RNA was extracted from CD3+ T-cells isolated using immunomagnetic beads (>95% purity) from PBMCs collected immediately before mobilization and 6 hours after AMD3100 administration or 5 days after G-CSF mobilization. The RT2 Profiler ™ PCR Array was used which contains pathway specific cytokine genes associated with TH1, TH2, and TH3 cells. Expression levels of 16 genes changed significantly (false discovery rate=0.10) from baseline following G-CSF mobilization; 9 genes were up-regulated and 7 genes were down-regulated from baseline. Five up-regulated and 4 down-regulated genes had greater than a 2-fold change in expression (Figure). In contrast, none of the 84 genes examined, including the 16 altered with G-CSF, changed significantly following AMD3100 administration. Our results are concordant with current literature that shows the expression of several genes effecting T-cell cytokine polarization are altered in G-CSF mobilized T-cells. It has been suggested that the TH2 polarization in G-CSF mobilized products contributes to the comparable incidence of acute GVHD and the higher incidence of chronic GVHD compared to bone marrow allografts. In contrast, T-cells mobilized with AMD3100 appear similar to non-mobilized T-cells, and do not undergo a change in TH1- and TH2-related gene expression. Whether the differences in cytokine polarization of T-lymphocytes mobilized with AMD3100 compared to G-CSF will impact immune reconstitution or other immune sequela (i.e. GVHD, graft-vs.-tumor) associated with HCT is currently being assessed in a pilot allogeneic transplantation trial in humans using AMD3100 to mobilize donors. Figure: Heat map showing expression levels of 16 genes in CD3+ T-cells that changed significantly from baseline following G-CSF mobilization in 5 healthy donors. Samples were analyzed with a two-sample paired t-test, and the corresponding p-values were evaluated based on the permutation technique at a 10% false discovery rate. All samples were normalized to the center of the mean of the pre G-CSF samples with black denoting up-regulated expression and white denoting down-regulated expression. Figure:. Heat map showing expression levels of 16 genes in CD3+ T-cells that changed significantly from baseline following G-CSF mobilization in 5 healthy donors. Samples were analyzed with a two-sample paired t-test, and the corresponding p-values were evaluated based on the permutation technique at a 10% false discovery rate. All samples were normalized to the center of the mean of the pre G-CSF samples with black denoting up-regulated expression and white denoting down-regulated expression.


2017 ◽  
Vol 27 (9) ◽  
pp. 2795-2808 ◽  
Author(s):  
Wei Jiang ◽  
Weichuan Yu

In genome-wide association studies, we normally discover associations between genetic variants and diseases/traits in primary studies, and validate the findings in replication studies. We consider the associations identified in both primary and replication studies as true findings. An important question under this two-stage setting is how to determine significance levels in both studies. In traditional methods, significance levels of the primary and replication studies are determined separately. We argue that the separate determination strategy reduces the power in the overall two-stage study. Therefore, we propose a novel method to determine significance levels jointly. Our method is a reanalysis method that needs summary statistics from both studies. We find the most powerful significance levels when controlling the false discovery rate in the two-stage study. To enjoy the power improvement from the joint determination method, we need to select single nucleotide polymorphisms for replication at a less stringent significance level. This is a common practice in studies designed for discovery purpose. We suggest this practice is also suitable in studies with validation purpose in order to identify more true findings. Simulation experiments show that our method can provide more power than traditional methods and that the false discovery rate is well-controlled. Empirical experiments on datasets of five diseases/traits demonstrate that our method can help identify more associations. The R-package is available at: http://bioinformatics.ust.hk/RFdr.html .


2020 ◽  
Author(s):  
Matteo Sesia ◽  
Stephen Bates ◽  
Emmanuel Candès ◽  
Jonathan Marchini ◽  
Chiara Sabatti

AbstractThis paper proposes a novel statistical method to address population structure in genome-wide association studies while controlling the false discovery rate, which overcomes some limitations of existing approaches. Our solution accounts for linkage disequilibrium and diverse ancestries by combining conditional testing via knockoffs with hidden Markov models from state-of-the-art phasing methods. Furthermore, we account for familial relatedness by describing the joint distribution of haplotypes sharing long identical-by-descent segments with a generalized hidden Markov model. Extensive simulations affirm the validity of this method, while applications to UK Biobank phenotypes yield many more discoveries compared to BOLT-LMM, most of which are confirmed by the Japan Biobank and FinnGen data.


2018 ◽  
Author(s):  
Keira J.A. Johnston ◽  
Mark J. Adams ◽  
Barbara I. Nicholl ◽  
Joey Ward ◽  
Rona J Strawbridge ◽  
...  

AbstractChronic pain is highly prevalent worldwide, with a significant socioeconomic burden, and also contributes to excess mortality. Chronic pain is a complex trait that is moderately heritable and genetically, as well as phenotypically, correlated with major depressive disorder (MDD). Use of the Conditional False Discovery Rate (cFDR) approach, which leverages pleiotropy identified from existing GWAS outputs, has been successful in discovering novel associated variants in related phenotypes. Here, genome-wide association study outputs for both von Korff chronic pain grade as a quasi-quantitative trait and for MDD were used to identify variants meeting a cFDR threshold for each outcome phenotype separately, as well as a conjunctional cFDR (ccFDR) threshold for both phenotypes together. Using a moderately conservative threshold, we identified a total of 11 novel single nucleotide polymorphisms (SNPs), six of which were associated with chronic pain grade and nine of which were associated with MDD. Four SNPs on chromosome 14 were associated with both chronic pain grade and MDD. SNPs associated only with chronic pain grade were located within SLC16A7 on chromosome 12. SNPs associated only with MDD were located either in a gene-dense region on chromosome 1 harbouring LINC01360, LRRIQ3, FPGT and FPGT-TNNI3K, or within/close to LRFN5 on chromosome 14. The SNPs associated with both outcomes were also located within LRFN5. Several of the SNPs on chromosomes 1 and 14 were identified as being associated with expression levels of nearby genes in the brain and central nervous system. Overall, using the cFDR approach, we identified several novel genetic loci associated with chronic pain and we describe likely pleiotropic effects of a recently identified MDD locus on chronic pain.Author SummaryGenetic variants explaining variation in complex traits can often be associated with more than one trait at once (‘pleiotropy’). Taking account of this pleiotropy in genetic studies can increase power to find sites in the genome harbouring trait-associated variants. In this study we used the suspected underlying pleiotropy between chronic pain and major depressive disorder to discover novel variants associated with chronic pain, and to investigate genetic variation that may be shared between the two disorders.


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