Fine-mapping is an analytical step to perform causal prioritization of the polymorphic variants on a trait-associated genomic region observed from genome-wide association studies (GWAS). The prioritization of causal variants can be challenging due to the linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. We propose a novel ℓ0 graph norm shrinkage algorithm to select causal variants from dense LD blocks consisting of highly correlated SNPs that may not be proximal or contiguous. We extract dense LD blocks and perform regression shrinkage to calculate a prioritization score to select a parsimonious set of causal variants. Our approach is computationally efficient and allows performing fine-mapping on thousands of polymorphisms. We demonstrate its application using a large UK Biobank (UKBB) sample related to nicotine addiction. Our results suggest that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Simulations were used to evaluate and compare the performance of our method and existing fine-mapping algorithms. The results demonstrated that our method outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate regarding causal variant selection. The application of this method to smoking severity trait in UKBB sample replicated previously reported loci and suggested the causal prioritization of genetic effects on nicotine dependency.Author summaryDisentangling the complex linkage disequilibrium (LD) pattern and selecting the underlying causal variants have been a long-term challenge for genetic fine-mapping. We find that the LD pattern within GWAS loci is intrinsically organized in delicate graph topological structures, which can be effectively learned by our novel ℓ0 graph norm shrinkage algorithm. The extracted LD graph structure is critical for causal variant selection. Moreover, our method is less constrained by the width of GWAS loci and thus can fine-map a massive number of correlated SNPs.
Smoking is a heritable behavior and nicotine dependency is complex mechanism supported by both positive and negative reinforcements. We hypothesized that cerebral white matter (WM) may mediate the individual dependency on nicotine integrity because its integrity is altered in smokers and shows dose-related response to nicotine administration. Two vertical and one horizontal pleiotropy pathways that combined individual genetic variations, measure of WM integrity by fractional anisotropy (FA), and nicotine dependence were evaluated in a large epidemiological sample (N=12,264 and 4,654 participants that have genetic, FA measure and nicotine dependence data available for smoking status and cigarettes per day (CPD), respectively) collected UK Biobank. We started by selecting the candidate genetic regions including genetic risk factors associated with smoking from genome-wide association study (GWAS) for causal pathway analysis. Then we identified pleiotropic loci that influence both nicotine dependence and WM integrity from these regions. We tested a horizontal pleiotropy pathway: (A) genetic risk factors associated with smoking were independently affecting both nicotine dependence and WM integrity. We also evaluated two vertical pleiotropy that assumed that individual genetic factors associated with nicotine dependence impacted B) impacted WM integrity which in turn led to higher nicotine dependence vs. C) led to nicotine dependence and resulting white matter alterations. There were 10 and 23 candidate pleiotropic variants identified for smoking status and CPD traits. All these variants exhibited vertical pleiotropy. For smoking status, the genetic effect on smoking status was mediated by FA measures over multiple brain regions. The variants were located in a gene SARDH, which catalyzes the oxidative demethylation of sarcosine that plays a role in reducing tolerance effect on nicotine. Conversely, CPD was a significant mediator in the vertical pleiotropy pathway to FA. The identified variants were located in gene IREB2, that was reported as a susceptibility gene for both neurodegeneration and smoking-induced diseases.
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