MARS: leveraging allelic heterogeneity to increase power of association testing.
Ontology highlight
ABSTRACT:
SUBMITTER: Hormozdiari F
PROVIDER: S-EPMC8086090 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:It is useful to detect allelic heterogeneity (AH), i.e., the presence of multiple causal SNPs in a locus, which, for example, may guide the development of new methods for fine mapping and determine how to interpret an appearing epistasis. In contrast to Mendelian traits, the existence and extent of AH for complex traits had been largely unknown until Hormozdiari et al. proposed a Bayesian method, called causal variants identification in associated regions (CAVIAR), and uncovered widespread AH in complex traits. However, there are several limitations with CAVIAR. First, it assumes a maximum number of causal SNPs in a locus, typically up to six, to save computing time; this assumption, as will be shown, may influence the outcome. Second, its computational time can be too demanding to be feasible since it examines all possible combinations of causal SNPs (under the assumed upper bound). Finally, it outputs a posterior probability of AH, which may be difficult to calibrate with a commonly used nominal significance level. Here, we introduce an intersection-union test (IUT) based on a joint/conditional regression model with all the SNPs in a locus to infer AH. We also propose two sequential IUT-based testing procedures to estimate the number of causal SNPs. Our proposed methods are applicable to not only individual-level genotypic and phenotypic data, but also genome-wide association study (GWAS) summary statistics. We provide numerical examples based on both simulated and real data, including large-scale schizophrenia (SCZ) and high-density lipoprotein (HDL) GWAS summary data sets, to demonstrate the effectiveness of the new methods. In particular, for both the SCZ and HDL data, our proposed IUT not only was faster, but also detected more AH loci than CAVIAR. Our proposed methods are expected to be useful in further uncovering the extent of AH in complex traits.
| S-EPMC6116971 | biostudies-literature
Project description:BACKGROUND:Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS:In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS:Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.
| S-EPMC7132874 | biostudies-literature
Project description:Genome-wide association studies (GWASs) have successfully identified many genetic variants and risk loci for complex traits and common diseases in the last 15 years. However, these identified variants, in general, can explain only a small to moderate proportion of the heritability, thus the task of improving GWAS power for more discoveries remains both critical and challenging. In addition to the usual but costly or even infeasible route of continuing to increase the sample size, many approaches have been proposed to incorporate functional annotations to prioritize SNPs but with only limited success. Here, by taking advantage of increasing availability of various types of omics data, we propose a new and orthogonal approach by integrating individual-level omics data with GWASs. The premise is that since omics data reflect both genetic and environmental (such as diet and other lifestyle) effects on individuals, they can be used to account for (otherwise unexplained) variations among individuals in GWAS analysis, leading to more precise/efficient estimation and thus higher power. As a concrete example, we propose boosting GWAS power by adjusting for metabolomics data in GWAS analysis. We applied the method to the UK Biobank subcohort of n = 90,000 individuals with both GWAS and metabolomics data. The analysis of 7 quantitative traits and one binary trait demonstrated clear power gains. For example, the new method (after adjusting for metabolomics data) identified 13 new loci for diastolic blood pressure that were all missed by the standard GWAS, and most or all of the 13 new signals were validated in two much larger GWAS datasets (n = 340,000 and 700,000); the improved estimation efficiency was equivalent to a 38.4% gain of GWAS sample size. The proposed method is both simple and promising and broadly applicable to integrating GWASs with other omics data.
| S-EPMC9547296 | biostudies-literature
Project description:BACKGROUND:Few studies have explored how economic incentives influence behavioral outcomes. This study aimed to identify pathways of action of an incentives-based intervention to increase men's participation in HIV testing. METHODS:The qualitative study was embedded in a randomized-controlled trial that compared effectiveness of gain-framed, loss-framed and lottery-based incentives to increase HIV testing among men. Following testing at a community health campaign, 60 in-depth interviews were conducted with men systematically sampled on the basis of age, incentive group, and campaign attendance. Data were coded deductively and inductively for thematic content analysis. RESULTS:Incentives addressed men's structural, interpersonal and individual-level barriers to testing: offered at convenient locations, incentives offset costs of testing, in lost wages, which are exacerbated when livelihoods required mobility. Interpersonal barriers included anticipated stigma/fear of disclosure, social obligations, and negative peer influences. Providing incentives in public settings provided "social proof" that prizes could be won, and facilitated social support and positive norms by promoting testing with trusted others. Incentives had little influence when men appraised prize values to be low, disbelieved they would win a prize, or were already intrinsically motivated to test. Yet, incentives provided a behavioral 'cue to action' for many men who perceived themselves to be susceptible to HIV and perceived HIV disease to be severe, acting as secondary motivator for testing that "sweetened the deal". CONCLUSION:Incentives can be an important 'lever' to promote men's healthy behaviors in resource-poor settings. HIV testing in convenient, public settings, when paired with incentives, provides multiple pathways to stimulate men's testing uptake. TRIAL REGISTRATION:Registered with ClinicalTrials.gov on 08/10/2016, ID: NCT02890459. The first participant was enrolled on 11th April 2016.
| S-EPMC6937741 | biostudies-literature
Project description:Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect.
| S-EPMC4795054 | biostudies-literature
Project description:In recent years, as a secondary analysis in genome-wide association studies (GWASs), conditional and joint multiple-SNP analysis (GCTA-COJO) has been successful in allowing the discovery of additional association signals within detected loci. This suggests that many loci mapped in GWASs harbor more than a single causal variant. In order to interpret the underlying mechanism regulating a complex trait of interest in each discovered locus, researchers must assess the magnitude of allelic heterogeneity within the locus. We developed a penalized selection operator for jointly analyzing multiple variants (SOJO) within each mapped locus on the basis of LASSO (least absolute shrinkage and selection operator) regression derived from summary association statistics. We found that, compared to stepwise conditional multiple-SNP analysis, SOJO provided better sensitivity and specificity in predicting the number of alleles associated with complex traits in each locus. SOJO suggested causal variants potentially missed by GCTA-COJO. Compared to using top variants from genome-wide significant loci in GWAS, using SOJO increased the proportion of variance prediction for height by 65% without additional discovery samples or additional loci in the genome. Our empirical results indicate that human height is not only a highly polygenic trait, but also has high allelic heterogeneity within its established hundreds of loci.
| S-EPMC5812891 | biostudies-other
Project description:BACKGROUND/AIMS:Meta-analysis of genetic association studies is a useful approach when individual investigations do not yield studywise significant results but the evidence across studies is modest and homogeneous. Current meta-analysis methods account for heterogeneity by down-weighting studies as a function of between-study variance. We contend that current approaches may obscure interesting phenomena in genetic association data. However, an appropriate approach to examining heterogeneity across studies is lacking. METHODS:We develop a novel approach, based on the EM algorithm, to detect allelic heterogeneity, identify subpopulations and assign studies to those subpopulations. We then apply these methods to the association between DTNBP1 and schizophrenia (Scz), one of the most studied relationships in complex disease genetics. We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the DTNBP1 locus. RESULTS:We explored heterogeneity in several ways including meta-regression and approaches aimed at exploring the mixture of heterogeneous studies at a particular SNP. We found significant evidence for a mixture of association distributions at multiple loci. CONCLUSION:We propose a novel approach that is broadly applicable and may be useful in large scale genetic association meta-analyses to detect significant allelic heterogeneity.
| S-EPMC7077085 | biostudies-literature
Project description:Recombination acts on the genetic map, not on the physical map. On the other hand, the physical map is usually more accurate. Choice of the genetic or physical map for positional cloning by allelic association depends on the goodness of fit of data to each map under an established model. Huntington disease illustrates the usual case in which the greater reliability of physical data outweighs recombinational heterogeneity. Hemochromatosis represents an exceptional case in which unrecognized recombinational heterogeneity retarded positional cloning for a decade. The Malecot model performs well for major genes, but no approach assuming either equilibrium or disequilibrium has been validated for oligogenes contributing to common disease. In this case of greatest interest, the power of allelic association relative to linkage is less clear than for major genes.
| S-EPMC21648 | biostudies-literature
Project description:Proteus syndrome is a mosaic disorder that can cause progressive postnatal overgrowth of nearly any organ or tissue. To date, Proteus syndrome has been exclusively associated with the mosaic c.49G > A p.(Glu17Lys) pathogenic variant in AKT1, a variant that is also present in many cancers. Here we describe an individual with severe Proteus syndrome who died at 7.5 yr of age from combined parenchymal and restrictive pulmonary disease. Remarkably, this individual was found to harbor a mosaic c.49_50delinsAG p.(Glu17Arg) variant in AKT1 at a variant allele fraction that ranged from <0.01 to 0.46 in fibroblasts established from an overgrown digit. This variant was demonstrated to be constitutively activating by phosphorylation of AKT(S473). These data document allelic heterogeneity for Proteus syndrome. We recommend that individuals with a potential clinical diagnosis of Proteus syndrome who are negative for the p.(Glu17Lys) variant be tested for other variants in AKT1.
| S-EPMC7304363 | biostudies-literature
Project description:With >70,000 yearly publications using mouse data, mouse models represent the best engrained research system to address numerous biological questions across all fields of science. Concerns of poor study and microbiome reproducibility also abound in the literature. Despite the well-known, negative-effects of data clustering on interpretation and study power, it is unclear why scientists often house >4 mice/cage during experiments, instead of ≤2. We hypothesized that this high animal-cage-density practice abounds in published literature because more mice/cage could be perceived as a strategy to reduce housing costs. Among other sources of 'artificial' confounding, including cyclical oscillations of the 'dirty-cage/excrement microbiome', we ranked by priority the heterogeneity of modern husbandry practices/perceptions across three professional organizations that we surveyed in the USA. Data integration (scoping-reviews, professional-surveys, expert-opinion, and 'implementability-score-statistics') identified Six-Actionable Recommendation Themes (SART) as a framework to re-launch emerging protocols and intuitive statistical strategies to use/increase study power. 'Cost-vs-science' discordance was a major aspect explaining heterogeneity, and scientists' reluctance to change. With a 'housing-density cost-calculator-simulator' and fully-annotated statistical examples/code, this themed-framework streamlines the rapid analysis of cage-clustered-data and promotes the use of 'study-power-statistics' to self-monitor the success/reproducibility of basic and translational research. Examples are provided to help scientists document analysis for study power-based sample size estimations using preclinical mouse data to support translational clinical trials, as requested in NIH/similar grants or publications.
| S-EPMC7081340 | biostudies-literature