@article {1191, title = {Genome-wide meta-analyses identify multiple loci associated with smoking behavior.}, journal = {Nat Genet}, volume = {42}, year = {2010}, month = {2010 May}, pages = {441-7}, abstract = {

Consistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], beta = 1.03, standard error (s.e.) = 0.053, P = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], beta = 0.367, s.e. = 0.059, P = 5.7 x 10(-10); and rs1028936[A], beta = 0.446, s.e. = 0.074, P = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], beta = 0.333, s.e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95\% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95\% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.

}, keywords = {Age Factors, Alleles, Chromosome Mapping, Female, Genome, Genome-Wide Association Study, Genotype, Humans, Male, Molecular Epidemiology, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Receptors, Nicotinic, Smoking}, issn = {1546-1718}, doi = {10.1038/ng.571} } @article {6556, title = {Effect of genetic variants associated with plasma homocysteine levels on stroke risk.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Jul}, pages = {1920-4}, abstract = {

BACKGROUND AND PURPOSE: Elevated total plasma homocysteine (tHcy) levels are known to be associated with increased risk of ischemic stroke (IS). Given that both tHcy and IS are heritable traits, we investigated a potential genetic relationship between homocysteine levels and stroke risk by assessing 18 polymorphisms previously associated with tHcy levels for their association with IS and its subtypes.

METHODS: Previous meta-analysis results from an international stroke collaborative network, METASTROKE, were used to assess association of the 18 tHcy-associated single-nucleotide polymorphisms (SNPs) in 12 389 IS cases and 62 004 controls. We also investigated the associations in regions located within 50 kb from the 18 tHcy-related SNPs and the association of a genetic risk score, including the 18 SNPs.

RESULTS: One SNP located in the RASIP1 gene and a cluster of 3 SNPs located at and near SLC17A3 were significantly associated with IS (P<0.0003) after correcting for multiple testing. For stroke subtypes, the sentinel SNP located upstream of MUT was significantly associated with small-vessel disease (P=0.0022), whereas 1 SNP located in MTHFR was significantly associated with large-vessel disease (P=0.00019). A genetic risk score, including the 18 SNPs, did not show significant association with IS or its subtypes.

CONCLUSIONS: This study found several potential associations with IS and its subtypes: an association of an MUT variant with small-vessel disease, an MTHFR variant with large-vessel disease, and associations of RASIP1 and SLC17A3 variants with overall IS.

}, keywords = {Brain Ischemia, Cohort Studies, Europe, Genetic Association Studies, Genetic Loci, Genetic Predisposition to Disease, Genetic Variation, Genome, Homocysteine, Humans, Polymorphism, Single Nucleotide, Risk, Stroke}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.114.005208}, author = {Cotlarciuc, Ioana and Malik, Rainer and Holliday, Elizabeth G and Ahmadi, Kourosh R and Par{\'e}, Guillaume and Psaty, Bruce M and Fornage, Myriam and Hasan, Nazeeha and Rinne, Paul E and Ikram, M Arfan and Markus, Hugh S and Rosand, Jonathan and Mitchell, Braxton D and Kittner, Steven J and Meschia, James F and van Meurs, Joyce B J and Uitterlinden, Andr{\'e} G and Worrall, Bradford B and Dichgans, Martin and Sharma, Pankaj} } @article {6296, title = {A robust method for genome-wide association meta-analysis with the application to circulating insulin-like growth factor I concentrations.}, journal = {Genet Epidemiol}, volume = {38}, year = {2014}, month = {2014 Feb}, pages = {162-71}, abstract = {

Genome-wide association studies (GWAS) offer an excellent opportunity to identify the genetic variants underlying complex human diseases. Successful utilization of this approach requires a large sample size to identify single nucleotide polymorphisms (SNPs) with subtle effects. Meta-analysis is a cost-efficient means to achieve large sample size by combining data from multiple independent GWAS; however, results from studies performed on different populations can be variable due to various reasons, including varied linkage equilibrium structures as well as gene-gene and gene-environment interactions. Nevertheless, one should expect effects of the SNP are more similar between similar populations than those between populations with quite different genetic and environmental backgrounds. Prior information on populations of GWAS is often not considered in current meta-analysis methods, rendering such analyses less optimal for the detecting association. This article describes a test that improves meta-analysis to incorporate variable heterogeneity among populations. The proposed method is remarkably simple in computation and hence can be performed in a rapid fashion in the setting of GWAS. Simulation results demonstrate the validity and higher power of the proposed method over conventional methods in the presence of heterogeneity. As a demonstration, we applied the test to real GWAS data to identify SNPs associated with circulating insulin-like growth factor I concentrations.

}, keywords = {Computer Simulation, Genetic Linkage, Genome, Genome-Wide Association Study, Humans, Insulin-Like Growth Factor I, Meta-Analysis as Topic, Models, Genetic, Polymorphism, Single Nucleotide, Sample Size}, issn = {1098-2272}, doi = {10.1002/gepi.21766}, author = {Wang, Tao and Zhou, Baiyu and Guo, Tingwei and Bidlingmaier, Martin and Wallaschofski, Henri and Teumer, Alexander and Vasan, Ramachandran S and Kaplan, Robert C} } @article {9113, title = {eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data.}, journal = {Brief Bioinform}, volume = {23}, year = {2022}, month = {2022 01 17}, abstract = {

Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.

}, keywords = {Genome, Genome-Wide Association Study, Genomics, Regulatory Sequences, Nucleic Acid, Whole Genome Sequencing}, issn = {1477-4054}, doi = {10.1093/bib/bbab497}, author = {Yang, Yingxi and Sun, Quan and Huang, Le and Broome, Jai G and Correa, Adolfo and Reiner, Alexander and Raffield, Laura M and Yang, Yuchen and Li, Yun} } @article {9253, title = {A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.}, journal = {Nat Methods}, volume = {19}, year = {2022}, month = {2022 Dec}, pages = {1599-1611}, abstract = {

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

}, keywords = {Genetic Variation, Genome, Genome-Wide Association Study, Humans, Phenotype, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-022-01640-x}, author = {Li, Zilin and Li, Xihao and Zhou, Hufeng and Gaynor, Sheila M and Selvaraj, Margaret Sunitha and Arapoglou, Theodore and Quick, Corbin and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Auer, Paul L and Bielak, Lawrence F and Bis, Joshua C and Blackwell, Thomas W and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Franceschini, Nora and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Lin, Bridget M and Manichaikul, Ani and Manning, Alisa K and Martin, Lisa W and Mathias, Rasika A and Meigs, James B and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Taylor, Kent D and Taub, Margaret A and Vasan, Ramachandran S and Weeks, Daniel E and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Willer, Cristen J and Natarajan, Pradeep and Peloso, Gina M and Lin, Xihong} }