Gene set analysis of genome-wide association studies: methodological issues and perspectives.

paper Cited Public

Authors: Wang, Lily; Jia, Peilin; Wolfinger, Russell D; Chen, Xi; Zhao, Zhongming
Year: 2011
Journal: Genomics
PMID: 21565265
DOI: 10.1016/j.ygeno.2011.04.006
PMCID: PMC3852939

#	Section	Preview
0	Introduction	Recently, genome-wide association studies (GWAS), which typically test disease associations with…
1	Introduction	To help prioritize association signals from GWAS and to better understand the biological themes…
2	Introduction	Current approaches for gene set analysis are still in an early stage of development. When different…
3	Introduction	In this review, we discuss the detailed procedures for gene set analysis, along with parameter…
4	Methodological issues	Figure 1 outlines the critical steps for assessing statistical significance of disease associations…
5	Methodological issues — From SNPs to genes	When defining gene boundaries, different criteria (e.g., 500kb [5], 200kb [10], 20kb [11], and 5kb…
6	Methodological issues — From SNPs to genes	of genes. One potentially promising strategy is to take advantage of the information from gene…
7	Methodological issues — From genes to gene sets	The Kyoto Encyclopedia of Genes and Genomes (KEGG) [20] and Gene Ontology (GO) [21] are frequently…
8	Methodological issues — From genes to gene sets	Redundancy among gene sets has often been observed because, by their nature, gene sets such as…
9	Methodological issues — From genes to gene sets	Another issue is that gene set annotation is still incomplete. So far, only about 5000 human genes…
10	Methodological issues — Formulating hypothesis	In the analysis of gene expression data, Tian et al. [27] formulated two statistical hypotheses for…
11	Methodological issues — Formulating hypothesis	as competitive and self-contained tests, respectively. While a competitive test compares disease…
12	Methodological issues — Constructing test statistics	A test statistic can be constructed with units based on either gene or SNP association signals. We…
13	Methodological issues — Constructing test statistics	However, in gene-based methods, a consensus has not been reached on the best strategy for SNP…
14	Methodological issues — Constructing test statistics	Recently, Ballard et al. [42] compared seven multi-marker association tests, including single marker…
15	Methodological issues — Potential sources of bias	When scoring gene sets, several sources of potential bias need to be considered: Linkage…
16	Methodological issues — Potential sources of bias	gene, gene set significance may be driven by only a few of these SNPs, because the significant SNPs…
17	Methodological issues — Potential sources of bias	all selection processes (e.g., selecting the most significant SNPs to represent each gene and…
18	Methodological issues — Potential sources of bias	There are other biases that might affect gene set analysis results including annotation biases from…
19	Methodological issues — Assessing statistical significance	To preserve LD patterns, permutations of sample labels are typically employed to establish null…

Name	Type
Androgen local	drug
bipolar disorder	phenotype
breast cancer	phenotype
causal SNP	cohort
complex diseases	phenotype
C-reactive protein	phenotype
Crohn’s disease	phenotype
cystic fibrosis	phenotype
disease	phenotype
disease risk	phenotype
disease status	phenotype
disease susceptibility	phenotype
Endometrial cancer local	phenotype
estrogen	drug
GAIN schizophrenia dataset local	cohort
gene	gene
Gene Ontology	drug
gene set local	drug
GeneSet local	gene
gene set annotation database local	drug
genetic variants	cohort
genome-wide association studies	cohort
GWAS	cohort
GWAS datasets local	cohort
HapMap	cohort
HapMap3	cohort
HNF1A local	gene
KEGG	drug
most significant SNP local	variant
MSigDB local	drug
PANTHER Classification System local	drug
phenotype of interest	phenotype
REACTOME local	drug
schizophrenia	phenotype
single nucleotide polymorphisms	variant
SNP	cohort
trait level local	phenotype
type 2 diabetes	phenotype
UGT1A1 local	gene
UGT1A10 local	gene
UGT1A3 local	gene
UGT1A4 local	gene
UGT1A5 local	gene
UGT1A6 local	gene
UGT1A7 local	gene
UGT1A8 local	gene
UGT1A9 local	gene

Citation	PMID	DOI	Status
Ackermann, M et al., BMC Bioinformatics, 2009, A general modular framework for gene set enrichment analysis	19192285	10.1186/1471-2105-10-47	Cited
Al-Shahrour, F et al., BMC Bioinformatics, 2007, From genes to functional classes in the study of biological systems	17407596	10.1186/1471-2105-8-114	Cited
Altshuler, DM et al., Nature, 2010, Integrating common and rare genetic variation in diverse human populations	20811451	10.1038/nature09298	Cited
Ashburner, M et al., Nat Genet, 2000, Gene ontology: tool for the unification of biology. The Gene Ontology Consortium	10802651	10.1038/75556	Cited
Askland, K et al., Hum Genet, 2009, Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission	19052778	10.1007/s00439-008-0600-y	Cited
Ballard, D et al., BMC Med Genomics, 2010, Pathway analysis comparison using Crohn's disease genome wide association studies	20584322	10.1186/1755-8794-3-25	Cited
Ballard, DH et al., Genet Epidemiol, 2010, Comparisons of multi-marker association methods to detect association between a candidate region and disease	19810024	10.1002/gepi.20448	Cited
Baranzini, SE et al., Hum Mol Genet, 2009, Pathway and network-based analysis of genome-wide association studies in multiple sclerosis	19286671	10.1093/hmg/ddp120	Cited
Baurley, JW et al., Stat Med, 2010, Discovery of complex pathways from observational data	20683892	10.1002/sim.3962	Cited
Benjamini, Y et al., Journal of the Royal Statistical Society, Series B, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing	—	—	—
Bonifaci, N et al., PLoS One, 2010, Exploring the link between germline and somatic genetic alterations in breast carcinogenesis	21124932	10.1371/journal.pone.0014078	Cited
Bush, WS et al., BioData Min, 2009, LD-spline: mapping SNPs on genotyping platforms to genomic regions using patterns of linkage disequilibrium	19954552	10.1186/1756-0381-2-7	Cited
Cantor, RM et al., Am. J. Hum. Genet, 2010, Prioritizing GWAS results: A review of statistical methods and recommendations for their application	20074509	10.1016/j.ajhg.2009.11.017	Cited
Chai, HS et al., BMC Bioinformatics, 2009, GLOSSI: a method to assess the association of genetic loci-sets with complex diseases	19344520	10.1186/1471-2105-10-102	Cited
Chasman, DI, Genet Epidemiol, 2008, On the utility of gene set methods in genomewide association studies of quantitative traits	18481796	10.1002/gepi.20334	Cited
Chen, L et al., Bioinformatics, 2009, Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways	19029127	10.1093/bioinformatics/btn613	Cited
Chen, LS et al., Am. J. Hum. Genet, 2010, Insights into Colon Cancer Etiology via a Regularized Approach to Gene Set Analysis of GWAS Data	20560206	10.1016/j.ajhg.2010.04.014	Cited
Chen, X et al., Genet Epidemiol, 2010, Pathway-based analysis for genome-wide association studies using supervised principal components	20842628	10.1002/gepi.20532	Cited
Churchill, GA et al., Genetics, 2008, Naive application of permutation testing leads to inflated type I error rates	18202402	10.1534/genetics.107.074609	Cited
De la Cruz, O et al., Genet. Epidemiol, 2009, Gene, region and pathway level analyses in whole-genome studies	20013942	10.1002/gepi.20452	Cited
Dinu, I et al., Brief Bioinform, 2009, Gene-set analysis and reduction	18836208	10.1093/bib/bbn042	Cited
Dinu, V et al., J Biomed Inform, 2007, Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis	17625973	10.1016/j.jbi.2007.06.002	Cited
Dudbridge, F et al., Genet Epidemiol, 2003, Rank truncated product of P-values, with application to genomewide association scans	14639705	10.1002/gepi.10264	Cited
Efron, B et al., Ann Appl Stat, 2007, On testing the significance of sets of genes	—	—	—
Efron, B, Statistical Science, 2008, Microarrays, empirical Bayes, and the two-groups model	—	—	—
Elbers, CC et al., Genet Epidemiol, 2009, Using genome-wide pathway analysis to unravel the etiology of complex diseases	19235186	10.1002/gepi.20395	Cited
Eleftherohorinou, H et al., PLoS One, 2009, Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases	19956648	10.1371/journal.pone.0008068	Cited
Frazer, KA et al., Nature, 2007, A second generation human haplotype map of over 3.1 million SNPs	17943122	10.1038/nature06258	Cited
Freudenberg, J et al., Hum Mol Genet, 2010, Locus category based analysis of a large genome-wide association study of rheumatoid arthritis	20639398	10.1093/hmg/ddq304	Cited
Gamazon, ER et al., Bioinformatics, 2010, SCAN: SNP and copy number annotation	19933162	10.1093/bioinformatics/btp644	Cited
Goeman, JJ et al., Bioinformatics, 2007, Analyzing gene expression data in terms of gene sets: methodological issues	17303618	10.1093/bioinformatics/btm051	Cited
Guo, YF et al., BMC Bioinformatics, 2009, A new permutation strategy of pathway-based approach for genome-wide association study	20021635	10.1186/1471-2105-10-429	Cited
Hoh, J et al., Genome Res, 2001, Trimming, weighting, and grouping SNPs in human case-control association studies	11731502	10.1101/gr.204001	Cited
Holden, M et al., Bioinformatics, 2008, GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies	18854360	10.1093/bioinformatics/btn516	Cited
Holmans, P et al., Am. J. Hum. Genet, 2009, Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder	19539887	10.1016/j.ajhg.2009.05.011	Cited
Hong, MG et al., Hum Genet, 2009, Strategies and issues in the detection of pathway enrichment in genome-wide association studies	19408013	10.1007/s00439-009-0676-z	Cited
Jia, P et al., Bioinformatics, 2011, dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks	21045073	10.1093/bioinformatics/btq615	Cited
Jia, P et al., Int J Comput Biol Drug Des, 2011, Assessing gene length biases in gene set analysis of Genome-Wide Association Studies	21297229	10.1504/IJCBDD.2010.038394	Cited
Jia, P et al., Int J Neuropsychopharmacol, 2011, Pathway-based analysis of GWAS datasets: effective but caution required	21208483	10.1017/S1461145710001446	Cited
Jia, P et al., PLoS ONE, 2011, Prioritization of epilepsy associated candidate genes by convergent analysis	21390307	10.1371/journal.pone.0017162	Cited
Jia, P et al., Schizophr Res, 2010, Common variants conferring risk of schizophrenia: a pathway analysis of GWAS data	20659789	10.1016/j.schres.2010.07.001	Cited
Kanehisa, M et al., Nucleic Acids Res, 2008, KEGG for linking genomes to life and the environment	18077471	10.1093/nar/gkm882	Cited
Karp, PD et al., Nucleic Acids Res, 2005, Expansion of the BioCyc collection of pathway/genome databases to 160 genomes	16246909	10.1093/nar/gki892	Cited
Kraft, P et al., Epidemiology, 2009, Complex diseases, complex genes: keeping pathways on the right track	19525687	10.1097/EDE.0b013e3181a93b98	Cited
Lambert, JC et al., J Alzheimers Dis, 2010, Implication of the immune system in Alzheimer's disease: evidence from genome-wide pathway analysis	20413860	10.3233/JAD-2010-100018	Cited
Lesnick, TG et al., PLoS Genet, 2007, A genomic pathway approach to a complex disease: axon guidance and Parkinson disease	17571925	10.1371/journal.pgen.0030098	Cited
Li, J et al., Breast Cancer Res Treat, 2010, A combined analysis of genome-wide association studies in breast cancer	20872241	10.1007/s10549-010-1172-9	Cited
Low, YL et al., PLoS Genet, 2010, Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility	20617168	10.1371/journal.pgen.1001012	Cited
Lunn, DJ et al., Genet Epidemiol, 2006, A Bayesian toolkit for genetic association studies	16544290	10.1002/gepi.20140	Cited
Luo, L et al., Eur J Hum Genet, 2010, Genome-wide gene and pathway analysis	20442747	10.1038/ejhg.2010.62	Cited
Manoli, T et al., Bioinformatics, 2006, Group testing for pathway analysis improves comparability of different microarray datasets	16895928	10.1093/bioinformatics/btl424	Cited
Manolio, TA et al., Nature, 2009, Finding the missing heritability of complex diseases	19812666	10.1038/nature08494	Cited
Marchini, J et al., Nat Genet, 2007, A new multipoint method for genome-wide association studies by imputation of genotypes	17572673	10.1038/ng2088	Cited
McCulloch, CE et al., Generalized, Linear and Mixed Models, 2001	—	—	—
Medina, I et al., Nucleic Acids Res, 2009, Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies	19502494	10.1093/nar/gkp481	Cited
Menashe, I et al., Cancer Res, 2010, Pathway analysis of breast cancer genome-wide association study highlights three pathways and one canonical signaling cascade	20460509	10.1158/0008-5472.CAN-09-4502	Cited
Mi, H et al., Nucleic Acids Res, 2010, PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium	20015972	10.1093/nar/gkp1019	Cited
Nam, D et al., Brief Bioinform, 2008, Gene-set approach for expression pattern analysis	18202032	10.1093/bib/bbn001	Cited
Nam, D et al., Nucleic Acids Res, 2010, GSA-SNP: a general approach for gene set analysis of polymorphisms	20501604	10.1093/nar/gkq428	Cited
O'Dushlaine, C et al., Bioinformatics, 2009, The SNP ratio test: pathway analysis of genome-wide association datasets	19620097	10.1093/bioinformatics/btp448	Cited
O'Dushlaine, C et al., Mol Psychiatry, 2011, Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility	20157312	10.1038/mp.2010.7	Cited
Pan, W, Hum Genet, 2008, Network-based model weighting to detect multiple loci influencing complex diseases	18719944	10.1007/s00439-008-0545-1	Cited
Peng, G et al., Eur J Hum Genet, 2010, Gene and pathway-based second-wave analysis of genome-wide association studies	19584899	10.1038/ejhg.2009.115	Cited
Perry, JR et al., Diabetes, 2009, Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach	19252133	10.2337/db08-1378	Cited
Reiner, AP et al., Am J Hum Genet, 2008, Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein	18439552	10.1016/j.ajhg.2008.03.017	Cited
Ruano, D et al., Am J Hum Genet, 2010, Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability	20060087	10.1016/j.ajhg.2009.12.006	Cited
Schaid, DJ et al., Am J Hum Genet, 2005, Nonparametric tests of association of multiple genes with human disease	15786018	10.1086/429838	Cited
Schwarz, DF et al., Bioinformatics, 2008, SNPtoGO: characterizing SNPs by enriched GO terms	18024970	10.1093/bioinformatics/btm551	Cited
Segre, AV et al., PLoS Genet, 2010, Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits	20714348	10.1371/journal.pgen.1001058	Cited
Servin, B et al., PLoS Genet, 2007, Imputation-based analysis of association studies: candidate regions and quantitative traits	17676998	10.1371/journal.pgen.0030114	Cited
Sidak, Z, J Am Stat Assoc, 1967, Rectangular confidence regions for the means of multivariate normal distributions	—	—	—
Simes, RJ, Biometrika, 1986, An improved Bonferroni procedure for multiple tests of significance	—	—	—
Stephens, M et al., Nat Rev Genet, 2009, Bayesian statistical methods for genetic association studies	19763151	10.1038/nrg2615	Cited
Subramanian, A et al., Proc Natl Acad Sci U S A, 2005, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles	16199517	10.1073/pnas.0506580102	Cited
Thomas, D, Nat Rev Genet, 2010, Gene-environment-wide association studies: emerging approaches	20212493	10.1038/nrg2764	Cited
Tian, L et al., Proc Natl Acad Sci U S A, 2005, Discovering statistically significant pathways in expression profiling studies	16174746	10.1073/pnas.0506577102	Cited
Tintle, NL et al., BMC Proc, 2009, Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16	20018093	10.1186/1753-6561-3-s7-s96	Cited
Torkamani, A et al., Genomics, 2008, Pathway analysis of seven common diseases assessed by genome-wide association	18722519	10.1016/j.ygeno.2008.07.011	Cited
Vastrik, I et al., Genome Biol, 2007, Reactome: a knowledge base of biologic pathways and processes	17367534	10.1186/gb-2007-8-3-r39	Cited
Veyrieras, JB et al., PLoS Genet, 2008, High-resolution mapping of expression-QTLs yields insight into human gene regulation	18846210	10.1371/journal.pgen.1000214	Cited
Wakefield, J, Am J Hum Genet, 2007, A Bayesian measure of the probability of false discovery in genetic epidemiology studies	17668372	10.1086/519024	Cited
Wang, K et al., Am J Hum Genet, 2009, Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease	19249008	10.1016/j.ajhg.2009.01.026	Cited
Wang, K et al., Am. J. Hum. Genet, 2007, Pathway-Based Approaches for Analysis of Genomewide Association Studies	17966091	10.1086/522374	Cited
Wang, K et al., Genet Epidemiol, 2008, A principal components regression approach to multilocus genetic association studies	17849491	10.1002/gepi.20266	Cited
Wang, K et al., Nat Rev Genet, 2010, Analysing biological pathways in genome-wide association studies	21085203	10.1038/nrg2884	Cited
Wang, L et al., Bioinformatics, 2011, An Efficient Hierarchical Generalized Linear Mixed Model for Testing Disease Association with Biological Pathways in Genome-wide Association Studies	21266443	10.1093/bioinformatics/btq728	Cited
Wu, J et al., Nat. Meth, 2009, Integrated network analysis platform for protein-protein interactions	19079255	10.1038/nmeth.1282	Cited
Yu, K et al., Genet. Epidemiol, 2009, Pathway analysis by adaptive combination of P-values	19333968	10.1002/gepi.20422	Cited
Zaykin, DV et al., Genet Epidemiol, 2002, Truncated product method for combining P-values	11788962	10.1002/gepi.0042	Cited
Zhang, K et al., Nucleic Acids Res, 2010, i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study	20435672	10.1093/nar/gkq324	Cited
Zhong, H et al., Am J Hum Genet, 2010, Integrating pathway analysis and genetics of gene expression for genome-wide association studies	20346437	10.1016/j.ajhg.2010.02.020	Cited

In this knowledge base

Title	Year	PMID
Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence.	2017	28196272
Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses.	2015	26110981
Endophenotypes for Alcohol Use Disorder: An Update on the Field.	2015	26236574
Polygenic risk for externalizing disorders: Gene-by-development and gene-by-environment effects in adolescents and young adults.	2015	25821660
Polygenic scores predict alcohol problems in an independent sample and show moderation by the environment.	2014	24727307
Pathway analysis of genomic data: concepts, methods, and prospects for future development.	2012	22480918

External

Title	Authors	Journal	Year	Link
Multi-trait GWAS for growth under contrasting thermal rearing conditions in rainbow trout (Oncorhynchus mykiss).	Gallardo-Hidalgo J et al.	—	2025	→
A best-match approach for gene set analyses in embedding spaces.	Li L et al.	—	2024	→
A genome-wide association study of Chinese and English language phenotypes in Hong Kong Chinese children.	Lin YP et al.	—	2024	→
A high-dimensional omnibus test for set-based association analysis.	Yang H et al.	—	2024	→
Novel insights into the genetic architecture of pregnancy glycemic traits from 14,744 Chinese maternities.	Zhu H et al.	—	2024	→
The goldmine of GWAS summary statistics: a systematic review of methods and tools.	Kontou PI et al.	—	2024	→
Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.	Tan X et al.	—	2024	→
A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis.	Manuel AM et al.	—	2023	→
The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study.	Kootbodien T et al.	—	2023	→
Toward Best Practices for Imaging Transcriptomics of the Human Brain.	Arnatkeviciute A et al.	—	2023	→
Variants in the CETP gene affect levels of HDL cholesterol by reducing the amount, and not the specific lipid transfer activity, of secreted CETP.	Ølnes ÅS et al.	—	2023	→
Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.	Woodward AA et al.	—	2022	→
Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis.	Zhang S et al.	—	2022	→
MEF2C gene variations are associated with ADHD in the Chinese Han population: a case-control study.	Fu X et al.	—	2022	→
Translational relevance of forward genetic screens in animal models for the study of psychiatric disease.	Sheardown E et al.	—	2022	→
An integrative study of genetic variants with brain tissue expression identifies viral etiology and potential drug targets of multiple sclerosis.	Manuel AM et al.	—	2021	→
ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING.	He Y et al.	—	2021	→
Gene-Based Tests of a Genome-Wide Association Study Dataset Highlight Novel Multiple Sclerosis Risk Genes.	Li H et al.	—	2021	→
Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis.	Esmaeili-Fard SM et al.	—	2021	→
Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study.	Kuo CY et al.	—	2021	→
Genome-wide association study and pathway analysis identify NTRK2 as a novel candidate gene for litter size in sheep.	Esmaeili-Fard SM et al.	—	2021	→
Integrating Multi-Omics Data for Gene-Environment Interactions.	Du Y et al.	—	2021	→
Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure.	Henarejos-Castillo I et al.	—	2021	→
Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data.	Fulcher BD et al.	—	2021	→
A Gene-Set Enrichment and Protein-Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle.	Srikanth K et al.	—	2020	→
An integrative, genomic, transcriptomic and network-assisted study to identify genes associated with human cleft lip with or without cleft palate.	Yan F et al.	—	2020	→
Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.	Das S et al.	—	2020	→
Functional annotation of melanoma risk loci identifies novel susceptibility genes.	Fang S et al.	—	2020	→
Genes acting in synapses and neuron projections are early targets of selection during urban colonization.	Mueller JC et al.	—	2020	→
Gene-set Enrichment with Mathematical Biology (GEMB).	Cochran AL et al.	—	2020	→
Genome-wide Identification of the Genetic Basis of Amyotrophic Lateral Sclerosis	Zhang S et al.	—	2020	—
Identification of Novel Genes Associated with Cortical Thickness in Alzheimer's Disease: Systems Biology Approach to Neuroimaging Endophenotype.	Kim BH et al.	—	2020	→
Pathway Analysis of Renal Cell Carcinoma Genome-Wide Association Studies Identifies Novel Associations.	Purdue MP et al.	—	2020	→
Polygenic Influences on Pubertal Timing and Tempo and Depressive Symptoms in Boys and Girls.	Horvath G et al.	—	2020	→
Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods.	Ebrahimpoor M et al.	—	2020	→
Tourette Syndrome Risk Genes Regulate Mitochondrial Dynamics, Structure, and Function.	Clarke RA et al.	—	2020	→
Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer.	Walsh N et al.	—	2019	→
An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.	Mallik S et al.	—	2019	→
A QTL on chromosome 3q23 influences processing speed in humans.	Knowles EEM et al.	—	2019	→
Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder.	Salvatore JE et al.	—	2019	→
Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data.	Mignogna KM et al.	—	2019	→
Gene-level genome-wide association analysis of suicide attempt, a preliminary study in a psychiatric Mexican population.	González-Castro TB et al.	—	2019	→
MicroRNA-655-3p and microRNA-497-5p inhibit cell proliferation in cultured human lip cells through the regulation of genes related to human cleft lip.	Gajera M et al.	—	2019	→
Non-Syndromic Cleft Lip with or without Cleft Palate: Genome-Wide Association Study in Europeans Identifies a Suggestive Risk Locus at 16p12.1 and Supports <i>SH3PXD2A</i> as a Clefting Susceptibility Gene.	van Rooij IA et al.	—	2019	→
Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic.	Sun R et al.	—	2019	→
The Genetics of Polycystic Ovary Syndrome: An Overview of Candidate Gene Systematic Reviews and Genome-Wide Association Studies.	Hiam D et al.	—	2019	→
Using targeted next-generation sequencing to characterize genetic differences associated with insecticide resistance in Culex quinquefasciatus populations from the southern U.S.	Kothera L et al.	—	2019	→
Validity of polygenic risk scores: are we measuring what we think we are?	Janssens ACJW	—	2019	→
A Biomolecular Network Driven Proteinic Interaction in HCV Clearance.	Singh P et al.	—	2018	→
Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.	Schumacher FR et al.	—	2018	→
Autism spectrum disorders and autistic traits share genetics and biology.	Bralten J et al.	—	2018	→
Computational systems biology approaches for Parkinson's disease.	Glaab E	—	2018	→
Genetic moderation of the effects of the Family Check-Up intervention on children's internalizing symptoms: A longitudinal study with a racially/ethnically diverse sample.	Lemery-Chalfant K et al.	—	2018	→
Genomic and Genotypic Characterization of <i>Cylindrospermopsis raciborskii</i>: Toward an Intraspecific Phylogenetic Evaluation by Comparative Genomics.	Abreu VAC et al.	—	2018	→
Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways.	Wu C et al.	—	2018	→
The Complex Interaction of Mitochondrial Genetics and Mitochondrial Pathways in Psychiatric Disease.	Cuperfain AB et al.	—	2018	→
Wild GWAS-association mapping in natural populations.	Santure AW et al.	—	2018	→
A Gene-Based Analysis of Acoustic Startle Latency.	Smith AK et al.	—	2017	→
A Novel Approach for Pathway Analysis of GWAS Data Highlights Role of BMP Signaling and Muscle Cell Differentiation in Colorectal Cancer Susceptibility.	Mishra A et al.	—	2017	→
A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways.	Liu Y et al.	—	2017	→
Association analysis identifies 65 new breast cancer risk loci.	Michailidou K et al.	—	2017	→
Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence.	Clark SL et al.	—	2017	→
Examination of the Involvement of Cholinergic-Associated Genes in Nicotine Behaviors in European and African Americans.	Melroy-Greif WE et al.	—	2017	→
Gene-wide Association Study Reveals RNF122 Ubiquitin Ligase as a Novel Susceptibility Gene for Attention Deficit Hyperactivity Disorder.	Garcia-Martínez I et al.	—	2017	→
Genomic Analysis of Genotype-by-Social Environment Interaction for <i>Drosophila melanogaster</i> Aggressive Behavior.	Rohde PD et al.	—	2017	→
Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA-SNP analysis.	Del Corvo M et al.	—	2017	→
Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.	Milne RL et al.	—	2017	→
Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions.	Kao PY et al.	—	2017	→
Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle.	Dadousis C et al.	—	2017	→
Pooling-Based Genome-Wide Association Study Identifies Risk Loci in the Pathogenesis of Ovarian Endometrioma in Chinese Han Women.	Wang W et al.	—	2017	→
The genetics of human longevity: an intricacy of genes, environment, culture and microbiome.	Dato S et al.	—	2017	→
The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis.	Debrabant B	—	2017	→
A novel type 2 diabetes risk allele increases the promoter activity of the muscle-specific small ankyrin 1 gene.	Yan R et al.	—	2016	→
Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes.	Rohde PD et al.	—	2016	→
Cross-Study Comparison Reveals Common Genomic, Network, and Functional Signatures of Desiccation Resistance in Drosophila melanogaster.	Telonis-Scott M et al.	—	2016	→
FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.	Huang J et al.	—	2016	→
Gene set analysis for interpreting genetic studies.	Pers TH	—	2016	→
Genetics of Coronary Artery Disease.	McPherson R et al.	—	2016	→
Genetics of structural connectivity and information processing in the brain.	Giddaluru S et al.	—	2016	→
Genetic variation in FAAH is associated with cannabis use disorders in a young adult sample of Mexican Americans.	Melroy-Greif WE et al.	—	2016	→
Genome-wide gene-based analysis suggests an association between Neuroligin 1 (NLGN1) and post-traumatic stress disorder.	Kilaru V et al.	—	2016	→
Investigation of a Possible Role for the Histidine Decarboxylase Gene in Tourette Syndrome in the Chinese Han Population: A Family-Based Study.	Dong H et al.	—	2016	→
Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.	Lin PL et al.	—	2016	→
Protein Quantitative Trait Loci Analysis Identifies Genetic Variation in the Innate Immune Regulator TOLLIP in Post-Lung Transplant Primary Graft Dysfunction Risk.	Cantu E et al.	—	2016	→
Targeted genomic analysis reveals widespread autoimmune disease association with regulatory variants in the TNF superfamily cytokine signalling network.	Richard AC et al.	—	2016	→
Test for association of common variants in GRM7 with alcohol consumption.	Melroy-Greif WE et al.	—	2016	→
The statistical properties of gene-set analysis.	de Leeuw CA et al.	—	2016	→
ABC transporters and the proteasome complex are implicated in susceptibility to Stevens-Johnson syndrome and toxic epidermal necrolysis across multiple drugs.	Nicoletti P et al.	—	2015	→
Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses.	Hart AB et al.	—	2015	→
A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants.	Pan W et al.	—	2015	→
Endophenotypes for Alcohol Use Disorder: An Update on the Field.	Salvatore JE et al.	—	2015	→
Genetic Analysis of Association Between Calcium Signaling and Hippocampal Activation, Memory Performance in the Young and Old, and Risk for Sporadic Alzheimer Disease.	Heck A et al.	—	2015	→
Genetic factors and epigenetic mechanisms of longevity: current perspectives.	Lazarus J et al.	—	2015	→
Integrating Diverse Types of Genomic Data to Identify Genes that Underlie Adverse Pregnancy Phenotypes.	Hirbo J et al.	—	2015	→
Interaction of Wnt pathway related variants with type 2 diabetes in a Chinese Han population.	Zhou JB et al.	—	2015	→
Involvement of astrocyte metabolic coupling in Tourette syndrome pathogenesis.	de Leeuw C et al.	—	2015	→
Lessons learned in the analysis of high-dimensional data in vaccinomics.	Oberg AL et al.	—	2015	→
Linking genes to neurological clinical practice: the genomic basis for neurorehabilitation.	Goldberg A et al.	—	2015	→
MAGMA: generalized gene-set analysis of GWAS data.	de Leeuw CA et al.	—	2015	→
META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies.	Rosenberger A et al.	—	2015	→
Pathway-Based Genome-Wide Association Studies for Plasma Triglycerides in Obese Females and Normal-Weight Controls.	Jiao H et al.	—	2015	→
Polygenic risk for externalizing disorders: Gene-by-development and gene-by-environment effects in adolescents and young adults.	Salvatore JE et al.	—	2015	→
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders.	Parikshak NN et al.	—	2015	→
Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.	Ghosh S et al.	—	2015	→
Converging genetic and functional brain imaging evidence links neuronal excitability to working memory, psychiatric disease, and brain activity.	Heck A et al.	—	2014	→
Explaining additional genetic variation in complex traits.	Robinson MR et al.	—	2014	→
Human longevity and variation in DNA damage response and repair: study of the contribution of sub-processes using competitive gene-set analysis.	Debrabant B et al.	—	2014	→
Integrative identification of deregulated miRNA/TF-mediated gene regulatory loops and networks in prostate cancer.	Afshar AS et al.	—	2014	→
Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.	Jia P et al.	—	2014	→
Pathway-based analysis tools for complex diseases: a review.	Jin L et al.	—	2014	→
Polygenic scores predict alcohol problems in an independent sample and show moderation by the environment.	Salvatore JE et al.	—	2014	→
Semantic particularity measure for functional characterization of gene sets using gene ontology.	Bettembourg C et al.	—	2014	→
The synapse in schizophrenia.	Pocklington AJ et al.	—	2014	→
Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index.	Simonson MA et al.	—	2014	→
A century after Fisher: time for a new paradigm in quantitative genetics.	Nelson RM et al.	—	2013	→
Association signals unveiled by a comprehensive gene set enrichment analysis of dental caries genome-wide association studies.	Wang Q et al.	—	2013	→
Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.	Kang C et al.	—	2013	→
Gene-based testing of interactions in association studies of quantitative traits.	Ma L et al.	—	2013	→
Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster.	De S et al.	—	2013	→
Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease.	Carbonetto P et al.	—	2013	→
Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.	International Multiple Sclerosis Genetics Consortium	—	2013	→
Pathway analysis using information from allele-specific gene methylation in genome-wide association studies for bipolar disorder.	Chuang LC et al.	—	2013	→
Pathway-based analysis using genome-wide association data from a Korean non-small cell lung cancer study.	Lee D et al.	—	2013	→
Pathway-based approaches for sequencing-based genome-wide association studies.	Wu G et al.	—	2013	→
Pharmacogenetics of topical and systemic treatment of psoriasis.	Prieto-Pérez R et al.	—	2013	→
Topics in transcriptional control of lipid metabolism: from transcription factors to gene-promoter polymorphisms.	Bergen WG et al.	—	2013	→
Translating genome wide association study results to associations among common diseases: in silico study with an electronic medical record.	Anand V et al.	—	2013	→
A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia.	Jia P et al.	—	2012	→
Cell adhesion molecules contribute to Alzheimer's disease: multiple pathway analyses of two genome-wide association studies.	Liu G et al.	—	2012	→
Comparison of pathway analysis approaches using lung cancer GWAS data sets.	Fehringer G et al.	—	2012	→
Enriched pathways for major depressive disorder identified from a genome-wide association study.	Kao CF et al.	—	2012	→
Genomics in 2012: challenges and opportunities in the next generation sequencing era.	Zhao Z et al.	—	2012	→
Integrated genome-wide pathway association analysis with INTERSNP.	Herold C et al.	—	2012	→
Integrative pathway analysis of genome-wide association studies and gene expression data in prostate cancer.	Jia P et al.	—	2012	→
Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.	Jia P et al.	—	2012	→
Pathway analysis of genomic data: concepts, methods, and prospects for future development.	Ramanan VK et al.	—	2012	→
Physiology and Endocrinology Symposium: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females.	Snelling WM et al.	—	2012	→
Pooled sample-based GWAS: a cost-effective alternative for identifying colorectal and prostate cancer risk variants in the Polish population.	Gaj P et al.	—	2012	→
Searching joint association signals in CATIE schizophrenia genome-wide association studies through a refined integrative network approach.	Jia P et al.	—	2012	→
Uncovering networks from genome-wide association studies via circular genomic permutation.	Cabrera CP et al.	—	2012	→
A comprehensive network and pathway analysis of candidate genes in major depressive disorder.	Jia P et al.	—	2011	→
An improved kinetic model for the acetone-butanol-ethanol pathway of Clostridium acetobutylicum and model-based perturbation analysis.	Li RD et al.	—	2011	→
Network-assisted Causal Gene Detection in Genome-wide Association Studies: An Improved Module Search Algorithm.	Jia P et al.	—	2011	→