Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits.

paper Cited Public

Authors: Segrè, Ayellet V; DIAGRAM Consortium; MAGIC investigators; Groop, Leif; Mootha, Vamsi K; Daly, Mark J; Altshuler, David
Year: 2010
Journal: PLoS genetics
PMID: 20714348
DOI: 10.1371/journal.pgen.1001058
PMCID: PMC2920848

Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA's performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and approximately 1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility.

Figure 1

Description of Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) method.(A) Step 1: Map genetic variants and their association scores onto genes. MAGENTA uses as input the association z-scores or p-values of DNA sequence variants across the entire genome. In this work, we used association p-values of single-nucleotide polymorphisms, SNPs (circles) from a genome-wide association study or meta-analysis, denoted as for SNP i. Gene boundaries (vertical dashed lines) are defined here as predetermined physical distances added upstream and downstream to the most extreme transcript start and end sites of the gene (red arrow), respectively. Linkage-based distances can also be used. Each gene is assigned a set of SNPs that fall in its gene region boundaries. Two genes are shown for simplicity. (B) Step 2: Score genes based on their local SNP . Here the most significant of all SNPs i that lie within the extended gene boundaries is assigned to each gene g in the genome (). (C) Step 3: Correct for confounding effects on the gene score, in the absence of genotype data. In this study we used step-wise multivariate linear regression analysis to regress out of the confounding effects of several physical and genetic properties of genes (listed in Table 1); refers to the corrected gene p-value for gene g. In cases where two genes are assigned the same best SNP p-value, tends to be more significant for small genes than for large genes. (D) Step 4: Calculate a gene set enrichment p-value for each biological pathway or gene set of interest. We used a non-parametric statistical test to test whether for all genes in gene set gs are enriched for highly ranked gene scores more than would be expected by chance, compared to randomly sampled gene sets of identical size from the genome. refers to the nominal gene set enrichment p-value for gene set gs.

LLM interpretation

This figure is a four-step schematic diagram illustrating the MAGENTA method for gene-set enrichment analysis. It depicts the workflow of mapping SNP association p-values to genes (A), assigning the most significant SNP p-value to each gene (B), correcting for confounders like gene size (C), and calculating a gene set enrichment p-value (D). The final panel compares an "enriched" gene set with a low p-value ($10^{-3}$) against a "not enriched" gene set with a high p-value (0.4), using a grayscale gradient to represent association strength.

Figure 2

Regression analysis corrects for majority of confounding effects on gene association scores in a genotype-independent manner.The performance of a step-wise regression analysis approach in correcting for confounders on was evaluated against permutation analysis correction, since the latter corrects for all confounders without requiring a priori knowledge of them. T2D gene association p-values were plotted for all genes g in the genome (A) before gene score adjustment () and (B) after correction for confounders using regression analysis (), as a function of corrected gene p-values using phenotype permutation analysis (). The Diabetes Genetics Initiative (DGI) GWA study was used for the analysis, since we had access to all individuals' genotypes. is the association p-value of the best regional SNP for gene g before correction (y-axis in A). To compute (y-axis in B), step-wise multivariate linear regression analysis was applied to against the first four confounders listed in Table 1 (this approach does not require genotype data). The Pearson's correlation coefficient (calculated between p-value vectors before log transformation) increased significantly following the regression-based correction (from r = 0.69 to r = 0.95). The spread around the diagonal (red line) also decreased following the regression correction (from a coefficient of variation (mean/std) of 1.13 to 0.56). The minimum is 10−4 as the p-values were calculated based on 1,000 permutations for genes with , and 10,000 permutations for genes with . Some of the variation in the low p-value tail is due to having done only 10,000 permutations (), and some to limitations of the linear regression method. Note that the four dots in (A) with contain ten overlapping dots that refer to four sets of 2–3 genes, each set assigned the same . Gene association p-values are plotted on a −log10(p-value) scale.

LLM interpretation

This figure consists of two scatter plots (A and B) comparing T2D gene association p-values against p-values corrected by phenotype permutation analysis, both plotted on a $-\log_{10}$ scale. Plot A shows the data before correction with a Pearson correlation coefficient of $r = 0.69$, while Plot B shows the data after step-wise regression correction, where the correlation increases to $r = 0.95$. In both panels, a red diagonal line represents the identity, with Plot B showing a tighter clustering of data points around this line compared to Plot A.

Figure 3

Estimating power of the GSEA algorithm in MAGENTA using computer simulations.We used simulations to assess the power (sensitivity) of the gene set enrichment analysis (GSEA) algorithm in MAGENTA to detect enrichment of genes with modest effect sizes that are hard to detect with single SNP analysis. Power is plotted as a function of fraction (A) or number (B) of causal genes of modest effect in gene sets of 25 (triangles), 100 (squares), or 1,000 (circles) genes. The modest effect size spiked into genes is equivalent to 1% power of detecting an association at genome-wide significance using single SNP analysis. A total of 100 causal genes in the genome were assumed here. Randomized vectors from case/control permutations of the DGI study were used as the background association values. Simulations were repeated 1,000 times for each unique set of parameters. Power was calculated as the fraction of times the simulated gene set received a <0.01. For specificity estimations we used SNPs with no effect size, sampled from a null distribution that assumes no association. The false positive rate of the method (1-specificity) was comparable to the p-value cutoff used (0.3–1.7%). Note the x-axis in both panels is on a log10 scale.

LLM interpretation

This figure consists of two line plots (A and B) showing the power of the GSEA algorithm to detect enrichment based on computer simulations. The y-axis for both panels represents the "Power of detecting enrichment, %," while the x-axes use log10 scales to show the "Fraction of causal genes in gene set" (A) and the "Number of causal genes in gene set" (B). In both panels, power increases as the fraction or number of causal genes increases, with different curves representing gene set sizes of 25 (triangles), 100 (squares), and 1,000 (circles) genes.

#	Section	Preview
60	Materials and Methods — Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) — Step 4: Gene set enrichment analysis of genome-wide association data	below). (iv) Finally, a nominal GSEA p-value, was calculated for each gene set gs, defined as the…
61	Materials and Methods — Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) — Step 4: Gene set enrichment analysis of genome-wide association data	To test the robustness of our GSEA results for the mitochondria-related gene sets, we applied an…
62	Materials and Methods — Identifying confounders on gene association scores	The potential confounding effects of six gene properties on the most significant SNP p-value, for…
63	Materials and Methods — Identifying confounders on gene association scores	This was calculated using the –indep option in PLINK that prunes SNPs based on the variance…
64	Materials and Methods — Identifying confounders on gene association scores	distance between the most extreme genetic markers within the gene boundaries for which genetic…
65	Materials and Methods — Permutation analysis of Diabetes Genetics Initiative GWA study	We used the Diabetes Genetics Initiative (DGI) GWA study [17] as a test case for developing MAGENTA,…
66	Materials and Methods — Permutation analysis of Diabetes Genetics Initiative GWA study	1,000 permutations, resulting in an association p-value, for each SNP i and each permutation. was…
67	Materials and Methods — Permutation analysis of Diabetes Genetics Initiative GWA study	The gene score vectors before correction () calculated for the 1,000 DGI permuted data sets were…
68	Materials and Methods — Permutation analysis of Diabetes Genetics Initiative GWA study	gene-specific null distributions while maintaining the physical and genetic structure of SNPs across…
69	Materials and Methods — Permutation analysis of Diabetes Genetics Initiative GWA study	The permuted for all SNPs i were also used for power simulations described in the next section.
70	Materials and Methods — Simulations used to estimate sensitivity and specificity of MAGENTA	We developed a simulation framework to evaluate the power of MAGENTA to identify enrichment of…
71	Materials and Methods — Simulations used to estimate sensitivity and specificity of MAGENTA	assigned a SNP of small effect size were also randomly chosen from all genes in the gene set. The…
72	Materials and Methods — Simulations used to estimate sensitivity and specificity of MAGENTA	The parameters used in the simulations are: (i) Gene set size of 25, 100 or 1000 genes; (ii)…
73	Materials and Methods — Simulations used to estimate sensitivity and specificity of MAGENTA	= 10 for a modest effect size (equivalent to 1% power of detection at genome-wide significance…
74	Materials and Methods — Simulations used to estimate sensitivity and specificity of MAGENTA	This simulation framework was also used to choose an optimal gene score enrichment cutoff, for our…
75	Materials and Methods — Gene sets analyzed with MAGENTA — Mitochondria-related gene sets	Of the 1,012 unique human mitochondrial genes described in MitoCarta [22], we analyzed 966 autosomal…
76	Materials and Methods — Gene sets analyzed with MAGENTA — Mitochondria-related gene sets	A list of 91 oxidative phosphorylation (OXPHOS) genes out of the 966 autosomal, mitochondrial genes…
77	Materials and Methods — Gene sets analyzed with MAGENTA — Mitochondria-related gene sets	A set of 16 known nuclear transcriptional regulators of mitochondrial functions was assembled based…
78	Materials and Methods — Gene sets analyzed with MAGENTA — Lipid- and lipoprotein-related gene sets	We tested 15 biological processes related to lipid, fatty acid and steroid metabolism defined by the…
79	Materials and Methods — Gene sets analyzed with MAGENTA — Lipid- and lipoprotein-related gene sets	In this paper we analyzed gene sets with an initial gene set size of 10 genes or more.

Citation	PMID	DOI	Status
Andrulionytè, L et al., Diabetologia, 2004, Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial.	15592662	10.1007/s00125-004-1577-2	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
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
Chai, H 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
Cho, YM et al., Diabetes Res Clin Pract, 2007, Genetic factors related to mitochondrial function and risk of diabetes mellitus.	17451836	10.1016/j.diabres.2007.01.052	Cited
Dumas, J et al., Diabetes Metab, 2009, Is skeletal muscle mitochondrial dysfunction a cause or an indirect consequence of insulin resistance in humans?	19349201	10.1016/j.diabet.2009.02.002	Cited
Dupuis, J et al., Nat Genet, 2010, New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.	20081858	10.1038/ng.520	Cited
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
Farris, W et al., Biochemistry, 2005, Alternative splicing of human insulin-degrading enzyme yields a novel isoform with a decreased ability to degrade insulin and amyloid beta-protein.	15850385	10.1021/bi0476578	Cited
Finck, BN et al., J Clin Invest, 2006, PGC-1 coactivators: inducible regulators of energy metabolism in health and disease.	16511594	10.1172/JCI27794	Cited
Finley, LWS et al., Ageing Res Rev, 2009, The coordination of nuclear and mitochondrial communication during aging and calorie restriction.	19491041	10.1016/j.arr.2009.03.003	Cited
Giguère, V, Endocr Rev, 2008, Transcriptional control of energy homeostasis by the estrogen-related receptors.	18664618	10.1210/er.2008-0017	Cited
Goffart, S et al., Exp Physiol, 2003, Regulation and co-ordination of nuclear gene expression during mitochondrial biogenesis.	12525853	10.1113/eph8802500	Cited
Hong, M 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, J et al., Molecular Biology Reports, The polymorphisms of UCP1 genes associated with fat metabolism, obesity and diabetes.	19444646	10.1007/s11033-009-9550-2	Cited
Jin, W et al., Clin Sci, 2009, Genetic determinants and molecular pathways in the pathogenesis of Type 2 diabetes.	19076063	10.1042/CS20080090	Cited
Kathiresan, S et al., Nat Genet, 2009, Common variants at 30 loci contribute to polygenic dyslipidemia.	19060906	10.1038/ng.291	Cited
Kelley, DE et al., Diabetes, 2002, Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes.	12351431	10.2337/diabetes.51.10.2944	Cited
Kelly, DP et al., Genes Dev, 2004, Transcriptional regulatory circuits controlling mitochondrial biogenesis and function.	15004004	10.1101/gad.1177604	Cited
Koves, TR et al., Cell Metabolism, Mitochondrial Overload and Incomplete Fatty Acid Oxidation Contribute to Skeletal Muscle Insulin Resistance.	18177724	10.1016/j.cmet.2007.10.013	Cited
Liang, F et al., Nat Rev Endocrinol, 2009, SIRT1 and insulin resistance.	19455179	10.1038/nrendo.2009.101	Cited
Lowell, BB et al., Science, 2005, Mitochondrial dysfunction and type 2 diabetes.	15662004	10.1126/science.1104343	Cited
Matthews, DR et al., Diabetologia, 1985, Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.	3899825	10.1007/BF00280883	Cited
Mootha, VK et al., Nat Genet, 2003, PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.	12808457	10.1038/ng1180	Cited
Mootha, VK et al., Proc Natl Acad Sci USA, 2004, Erralpha and Gabpa/b specify PGC-1alpha-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle.	15100410	10.1073/pnas.0401401101	Cited
Myers, S et al., Science, 2005, A fine-scale map of recombination rates and hotspots across the human genome.	16224025	10.1126/science.1117196	Cited
Neale, BM et al., Am J Hum Genet, 2004, The future of association studies: gene-based analysis and replication.	15272419	10.1086/423901	Cited
Pagliarini, DJ et al., Cell, 2008, A mitochondrial protein compendium elucidates complex I disease biology.	18614015	10.1016/j.cell.2008.06.016	Cited
Patti, ME et al., Proc Natl Acad Sci USA, 2003, Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: Potential role of PGC1 and NRF1.	12832613	10.1073/pnas.1032913100	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, JRB et al., Diabetes, 2009, Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach.	19252133	10.2337/db08-1378	Cited
Petersen, KF et al., N Engl J Med, 2004, Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes.	14960743	10.1056/NEJMoa031314	Cited
Purcell, S et al., Bioinformatics, 2003, Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits.	12499305	10.1093/bioinformatics/19.1.149	Cited
Purcell, SM et al., Nature, 2009, Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.	19571811	10.1038/nature08185	Cited
Reiling, E et al., Eur J Hum Genet, 2009, Genetic association analysis of 13 nuclear-encoded mitochondrial candidate genes with type II diabetes mellitus: the DAMAGE study.	19209188	10.1038/ejhg.2009.4	Cited
Saccone, SF et al., Hum Mol Genet, 2007, Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs.	17135278	10.1093/hmg/ddl438	Cited
Saxena, R et al., Am J Hum Genet, 2006, Comprehensive association testing of common mitochondrial DNA variation in metabolic disease.	16773565	10.1086/504926	Cited
Saxena, R et al., Nat Genet, 2010, Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge.	20081857	10.1038/ng.521	Cited
Saxena, R et al., Science, 2007, Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.	17463246	10.1126/science.1142358	Cited
Schiff, M et al., J Inherit Metab Dis, 2009, Mitochondria and diabetes mellitus: untangling a conflictive relationship?	19821144	10.1007/s10545-009-1263-0	Cited
Scott, LJ et al., Science, 2007, A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.	17463248	10.1126/science.1142382	Cited
Sokal, RR et al., 1995, Biometry: The principles and Practice of Statistics in Biological Research. Third Edition.	—	—	—
Subramanian, A et al., Proc Natl Acad Sci USA, 2005, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.	16199517	10.1073/pnas.0506580102	Cited
Tapper, W et al., Proc Natl Acad Sci USA, 2005, A map of the human genome in linkage disequilibrium units.	16091463	10.1073/pnas.0505262102	Cited
Taylor, RW et al., Nat Rev Genet, 2005, Mitochondrial DNA mutations in human disease.	15861210	10.1038/nrg1606	Cited
Thomas, PD et al., Genome Research, 2003, PANTHER: A Library of Protein Families and Subfamilies Indexed by Function.	12952881	10.1101/gr.772403	Cited
Turner, N et al., Trends Endocrinol Metab, 2008, Is mitochondrial dysfunction a cause of insulin resistance?	18804383	10.1016/j.tem.2008.08.001	Cited
Veyrieras, J et al., PLoS Genet, 2008, High-resolution mapping of expression-QTLs yields insight into human gene regulation.	18846210	10.1371/journal.pgen.1000214	Cited
Voight, BF et al., Nature Genetics, 2010, Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.	20581827	10.1038/ng.609	Cited
Wan, J et al., Biochem Biophys Res Commun, 2009, Activation of PPARdelta up-regulates fatty acid oxidation and energy uncoupling genes of mitochondria and reduces palmitate-induced apoptosis in pancreatic beta-cells.	20040361	10.1016/j.bbrc.2009.12.127	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., 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
Yu, J et al., Ann N Y Acad Sci, 2009, The role of sirtuins in the control of metabolic homeostasis.	19751409	10.1111/j.1749-6632.2009.04952.x	Cited
Zeggini, E et al., Nat Genet, 2008, Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes.	18372903	10.1038/ng.120	Cited
Zeggini, E et al., Science, 2007, Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.	17463249	10.1126/science.1142364	Cited

In this knowledge base

Title	Year	PMID
Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials.	2018	28409280
Functional mapping and annotation of genetic associations with FUMA.	2017	29184056
Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets.	2017	28166213
Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis.	2017	27021816
Genetic studies of body mass index yield new insights for obesity biology.	2015	25673413
Defining the role of common variation in the genomic and biological architecture of adult human height.	2014	25282103
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.	2014	25231870
Pathway analysis of genomic data: concepts, methods, and prospects for future development.	2012	22480918
Gene set analysis of genome-wide association studies: methodological issues and perspectives.	2011	21565265
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.	2010	20935630
Hundreds of variants clustered in genomic loci and biological pathways affect human height.	2010	20881960
Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.	2010	21102462

External

Title	Authors	Journal	Year	Link
A review of post-GWAS studies in schizophrenia.	Maserrat S et al.	—	2025	→
Differential interactions between gene expressions and stressors across the lifespan in major depressive disorder.	Wang R et al.	—	2024	→
The goldmine of GWAS summary statistics: a systematic review of methods and tools.	Kontou PI et al.	—	2024	→
A genome-wide association study of frailty identifies significant genetic correlation with neuropsychiatric, cardiovascular, and inflammation pathways.	Ye Y et al.	—	2023	→
A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets.	Narganes-Carlón D et al.	—	2023	→
Causal associations between cardiorespiratory fitness and type 2 diabetes.	Cai L et al.	—	2023	→
CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.	Iyer G et al.	—	2023	→
Cross-tissue omics analysis discovers ten adipose genes encoding secreted proteins in obesity-related non-alcoholic fatty liver disease.	Darci-Maher N et al.	—	2023	→
Cross-Tissue Single-Nucleus RNA Sequencing Discovers Tissue-Resident Adipocytes Involved in Propanoate Metabolism in the Human Heart.	Das SS et al.	—	2023	→
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer.	Ji X et al.	—	2023	→
HGFAC is a ChREBP-regulated hepatokine that enhances glucose and lipid homeostasis.	Sargsyan A et al.	—	2023	→
Hierarchical association of COPD to principal genetic components of biological systems.	Carlin DE et al.	—	2023	→
Increased body mass index is linked to systemic inflammation through altered chromatin co-accessibility in human preadipocytes.	Garske KM et al.	—	2023	→
Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.	Yu S et al.	—	2023	→
Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation.	Selewa A et al.	—	2023	→
Small RNA Sequencing Analysis of STZ-Injured Pancreas Reveals Novel MicroRNA and Transfer RNA-Derived RNA with Biomarker Potential for Diabetes Mellitus.	Mo F et al.	—	2023	→
A cognitive neurogenetic approach to uncovering the structure of executive functions.	Feng J et al.	—	2022	→
A genome-wide analysis of 340 318 participants identifies four novel loci associated with the age of first spectacle wear.	Patasova K et al.	—	2022	→
Alcohol use disorder is associated with DNA methylation-based shortening of telomere length and regulated by TESPA1: implications for aging.	Jung J et al.	—	2022	→
Considering strategies for SNP selection in genetic and polygenic risk scores.	St-Pierre J et al.	—	2022	→
Deep learning enables genetic analysis of the human thoracic aorta.	Pirruccello JP et al.	—	2022	→
DNA methylation clocks for dogs and humans.	Horvath S et al.	—	2022	→
Enlightening the molecular mechanisms of type 2 diabetes with a novel pathway clustering and pathway subnetwork approach.	Bakir-Gungor B et al.	—	2022	→
Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy.	Sobrin L et al.	—	2022	→
Genome-Wide Association Study Identifies Two Common Loci Associated with Pigment Dispersion Syndrome/Pigmentary Glaucoma and Implicates Myopia in its Development.	Simcoe MJ et al.	—	2022	→
Identification of New Biological Pathways Involved in Skin Aging From the Analysis of French Women Genome-Wide Data.	Rahmouni M et al.	—	2022	→
Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.	Vlietstra WJ et al.	—	2022	→
Information enhanced model selection for Gaussian graphical model with application to metabolomic data.	Zhou J et al.	—	2022	→
Population-based meta-analysis and gene-set enrichment identifies FXR/RXR pathway as common to fatty liver disease and serum lipids.	Handelman SK et al.	—	2022	→
Shared genetic mechanism between type 2 diabetes and COVID-19 using pathway-based association analysis.	Wu KCH et al.	—	2022	→
The role of mitochondrial reactive oxygen species in insulin resistance.	Ayer A et al.	—	2022	→
A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus.	Hardcastle AJ et al.	—	2021	→
Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal.	Uffelmann E et al.	—	2021	→
Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group.	Jia T et al.	—	2021	→
Fibrin is a critical regulator of neutrophil effector function at the oral mucosal barrier.	Silva LM et al.	—	2021	→
Genetic factors influencing a neurobiological substrate for psychiatric disorders.	Andlauer TFM et al.	—	2021	→
Genetic insights into biological mechanisms governing human ovarian ageing.	Ruth KS et al.	—	2021	→
Genetic Profile of Endotoxemia Reveals an Association With Thromboembolism and Stroke.	Leskelä J et al.	—	2021	→
Genome-wide association studies in Samoans give insight into the genetic architecture of fasting serum lipid levels.	Carlson JC et al.	—	2021	→
Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color.	Simcoe M et al.	—	2021	→
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability.	Gupta R et al.	—	2021	→
Huntington's Disease Pathogenesis: Two Sequential Components.	Hong EP et al.	—	2021	→
Immune cells lacking Y chromosome show dysregulation of autosomal gene expression.	Dumanski JP et al.	—	2021	→
Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies.	Marczyk M et al.	—	2021	→
Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis.	Jung SY et al.	—	2021	→
Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers.	Jung SY	—	2021	→
Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.	Silberstein M et al.	—	2021	→
Plasma Vitamin C and Type 2 Diabetes: Genome-Wide Association Study and Mendelian Randomization Analysis in European Populations.	Zheng JS et al.	—	2021	→
Using common genetic variants to find drugs for common epilepsies.	Mirza N et al.	—	2021	→
Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.	Lancour D et al.	—	2020	→
Analysis of Glucocorticoid-Related Genes Reveal <i>CCHCR1</i> as a New Candidate Gene for Type 2 Diabetes.	Brenner LN et al.	—	2020	→
BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis.	Hukku A et al.	—	2020	→
CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.	Bouland GA et al.	—	2020	→
Epigenetic aging is accelerated in alcohol use disorder and regulated by genetic variation in APOL2.	Luo A 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	→
Genome-wide association study identifies locus at chromosome 2q32.1 associated with syncope and collapse.	Hadji-Turdeghal K et al.	—	2020	→
Genome-wide association study of corneal biomechanical properties identifies over 200 loci providing insight into the genetic etiology of ocular diseases.	Simcoe MJ et al.	—	2020	→
Genome-Wide Association Study of Opioid Cessation.	Cox JW et al.	—	2020	→
Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan.	Hollis B et al.	—	2020	→
Hierarchical structural component model for pathway analysis of common variants.	Jiang N et al.	—	2020	→
Integrated genetic, epigenetic, and gene set enrichment analyses identify NOTCH as a potential mediator for PTSD risk after trauma: Results from two independent African cohorts.	Conrad D et al.	—	2020	→
Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia.	Hysi PG et al.	—	2020	→
Multi-trait analysis of rare-variant association summary statistics using MTAR.	Luo L et al.	—	2020	→
Pathway Analysis for Targeted and Untargeted Metabolomics.	Karnovsky A et al.	—	2020	→
Pleiotropy-Based Decomposition of Genetic Risk Scores: Association and Interaction Analysis for Type 2 Diabetes and CAD.	Chasman DI et al.	—	2020	→
Quantifying genetic effects on disease mediated by assayed gene expression levels.	Yao DW et al.	—	2020	→
Single-cell lineage analysis reveals extensive multimodal transcriptional control during directed beta-cell differentiation.	Weng C et al.	—	2020	→
Transethnic Meta-Analysis of Genome-Wide Association Studies Identifies Three New Loci and Characterizes Population-Specific Differences for Coronary Artery Disease.	Matsunaga H et al.	—	2020	→
TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.	Chatzinakos C et al.	—	2020	→
Wnt receptor gene <i>FZD1</i> was associated with schizophrenia in genome-wide SNP analysis of the Australian Schizophrenia Research Bank cohort.	Liu X et al.	—	2020	→
A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome.	Misselbeck K 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	→
Epigenome-Wide Association Study of Incident Type 2 Diabetes in a British Population: EPIC-Norfolk Study.	Cardona A et al.	—	2019	→
Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.	Flannick J et al.	—	2019	→
Genes with High Network Connectivity Are Enriched for Disease Heritability.	Kim SS et al.	—	2019	→
Genetic predisposition to mosaic Y chromosome loss in blood.	Thompson DJ et al.	—	2019	→
Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms.	Jones SE et al.	—	2019	→
Genome-wide association study identifies a novel locus associated with psychological distress in the Japanese population.	Koshimizu H et al.	—	2019	→
GWAS of Behavioral Traits.	Mehta D et al.	—	2019	→
GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.	Couto Alves A et al.	—	2019	→
Identification of two novel breast cancer loci through large-scale genome-wide association study in the Japanese population.	Low SK et al.	—	2019	→
Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.	Warrington NM et al.	—	2019	→
Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field.	Malatras A et al.	—	2019	→
Pathway analysis with genome-wide association study (GWAS) data detected the association of atrial fibrillation with the mTOR signaling pathway.	Ebana Y et al.	—	2019	→
Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic.	Sun R et al.	—	2019	→
Strategies for Network GWAS Evaluated Using Classroom Crowd Science.	Fong SH et al.	—	2019	→
The great hairball gambit.	Flint J et al.	—	2019	→
The molecular genetics of hand preference revisited.	de Kovel CGF et al.	—	2019	→
A Genome-Wide Search for Bipolar Disorder Risk Loci Modified by Mitochondrial Genome Variation.	Ryu E et al.	—	2018	→
A Meta-Analysis of Genome-Wide Association Studies of Growth Differentiation Factor-15 Concentration in Blood.	Jiang J et al.	—	2018	→
An integrative approach for building personalized gene regulatory networks for precision medicine.	van der Wijst MGP et al.	—	2018	→
A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.	Uppu S et al.	—	2018	→
Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.	Lin H et al.	—	2018	→
Comprehensive pathway analyses of schizophrenia risk loci point to dysfunctional postsynaptic signaling.	Schijven D et al.	—	2018	→
Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials.	Latendresse SJ et al.	—	2018	→
Differential Gene Expression in Macrophages From Human Atherosclerotic Plaques Shows Convergence on Pathways Implicated by Genome-Wide Association Study Risk Variants.	Chai JT et al.	—	2018	→
Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2.	Yoon S et al.	—	2018	→
Elucidating the genetic architecture of reproductive ageing in the Japanese population.	Horikoshi M et al.	—	2018	→
Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study.	Chattopadhyay S et al.	—	2018	→
Gene-based association study for lipid traits in diverse cohorts implicates <i>BACE1</i> and <i>SIDT2</i> regulation in triglyceride levels.	Andaleon A et al.	—	2018	→
Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder.	Mühleisen TW et al.	—	2018	→
Genetic Determinants for Leisure-Time Physical Activity.	Lin X et al.	—	2018	→
Genetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks.	Woo HJ et al.	—	2018	→
Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma.	Khawaja AP et al.	—	2018	→
Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis.	Waage J et al.	—	2018	→
Genome-wide association study identifies seven novel susceptibility loci for primary open-angle glaucoma.	Shiga Y et al.	—	2018	→
Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics.	Beaumont RN et al.	—	2018	→
Genome-wide meta-analysis and replication studies in multiple ethnicities identify novel adolescent idiopathic scoliosis susceptibility loci.	Khanshour AM et al.	—	2018	→
Genome-Wide Meta-Analysis Unravels Interactions between Magnesium Homeostasis and Metabolic Phenotypes.	Corre T et al.	—	2018	→
GWAS of epigenetic aging rates in blood reveals a critical role for TERT.	Lu AT et al.	—	2018	→
Integrating genome-wide association study summaries and element-gene interaction datasets identified multiple associations between elements and complex diseases.	He A et al.	—	2018	→
Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity.	Sakaue S et al.	—	2018	→
Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes.	Zhu X et al.	—	2018	→
Locus and gene-based GWAS meta-analysis identifies new diabetic nephropathy genes.	Saeed M	—	2018	→
Multi-ethnic genome-wide association study for atrial fibrillation.	Roselli C et al.	—	2018	→
Multi-level genomic analyses suggest new genetic variants involved in human memory.	Zhu Z et al.	—	2018	→
Nature vs. nurture in human sociality: multi-level genomic analyses of social conformity.	Chen B et al.	—	2018	→
One for all and all for One: Improving replication of genetic studies through network diffusion.	Lancour D et al.	—	2018	→
Patterns of differential gene expression in a cellular model of human islet development, and relationship to type 2 diabetes predisposition.	Perez-Alcantara M et al.	—	2018	→
Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern.	Zhang H et al.	—	2018	→
Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia.	Kauppi K et al.	—	2018	→
Whole genome sequence analyses of brain imaging measures in the Framingham Study.	Sarnowski C et al.	—	2018	→
A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway Is Associated With Major Depressive Disorder.	Zeng Y 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 peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory.	Freytag V et al.	—	2017	→
A Pilot Genome-Wide Association Study Identifies Potential Metabolic Pathways Involved in Tinnitus.	Gilles A et al.	—	2017	→
Common variants in ZMIZ1 and near NGF confer risk for primary dysmenorrhoea.	Li Z et al.	—	2017	→
Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network.	Sandor C et al.	—	2017	→
Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci.	Kar SP et al.	—	2017	→
Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes.	Dawed AY et al.	—	2017	→
Exome-wide Association Study Identifies CLEC3B Missense Variant p.S106G as Being Associated With Extreme Longevity in East Asian Populations.	Tanisawa K et al.	—	2017	→
Functional mapping and annotation of genetic associations with FUMA.	Watanabe K et al.	—	2017	→
Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.	Tragante V et al.	—	2017	→
Genetic architecture of age-related cognitive decline in African Americans.	Raj T et al.	—	2017	→
Genetic architecture of epigenetic and neuronal ageing rates in human brain regions.	Lu AT et al.	—	2017	→
Genetic correlation between amyotrophic lateral sclerosis and schizophrenia.	McLaughlin RL et al.	—	2017	→
Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility.	Wright DJ et al.	—	2017	→
Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets.	Wain LV et al.	—	2017	→
Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia.	Li Z et al.	—	2017	→
Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections.	Tian C et al.	—	2017	→
Genome-wide association study identifies 112 new loci for body mass index in the Japanese population.	Akiyama M et al.	—	2017	→
Genome-wide Association Study of Idiopathic Osteonecrosis of the Femoral Head.	Sakamoto Y et al.	—	2017	→
Genome-wide genetic analyses highlight mitogen-activated protein kinase (MAPK) signaling in the pathogenesis of endometriosis.	Uimari O et al.	—	2017	→
Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk.	Day FR et al.	—	2017	→
GWAS of epigenetic ageing rates in blood reveals a critical role for<i>TERT</i>	Lu AT et al.	—	2017	—
Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis.	Kemp JP et al.	—	2017	→
Identification of six new genetic loci associated with atrial fibrillation in the Japanese population.	Low SK et al.	—	2017	→
Implicating candidate genes at GWAS signals by leveraging topologically associating domains.	Way GP et al.	—	2017	→
Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness.	Willems SM et al.	—	2017	→
Large-scale interaction effects reveal missing heritability in schizophrenia, bipolar disorder and posttraumatic stress disorder.	Woo HJ et al.	—	2017	→
LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.	Zheng J et al.	—	2017	→
Meta-analysis of genome-wide SNP- and pathway-based associations for facets of neuroticism.	Kim SE et al.	—	2017	→
Novel Gene and Network Associations Found for Acute Lymphoblastic Leukemia Using Case-Control and Family-Based Studies in Multiethnic Populations.	Nakka P et al.	—	2017	→
Pancreatic Islet Protein Complexes and Their Dysregulation in Type 2 Diabetes.	Pedersen HK et al.	—	2017	→
Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions.	Kao PY et al.	—	2017	→
Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis.	Minicã CC et al.	—	2017	→
Ranking metrics in gene set enrichment analysis: do they matter?	Zyla J et al.	—	2017	→
Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers.	Pilling LC et al.	—	2017	→
Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.	Liang J et al.	—	2017	→
SLC9A9 Co-expression modules in autism-associated brain regions.	Patak J et al.	—	2017	→
Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease.	Moreno-Moral A et al.	—	2017	→
The Genetic Landscape of Renal Complications in Type 1 Diabetes.	Sandholm N et al.	—	2017	→
The NCAM1 gene set is linked to depressive symptoms and their brain structural correlates in healthy individuals.	Petrovska J et al.	—	2017	→
Tissue-specific pathway association analysis using genome-wide association study summaries.	Wang W et al.	—	2017	→
Update on the genetic architecture of rheumatoid arthritis.	Kim K et al.	—	2017	→
A genome-wide association meta-analysis of diarrhoeal disease in young children identifies FUT2 locus and provides plausible biological pathways.	Bustamante M et al.	—	2016	→
A genome-wide investigation of food addiction.	Cornelis MC et al.	—	2016	→
An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder.	Akula N et al.	—	2016	→
A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.	Zhang H et al.	—	2016	→
A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci.	Qian J et al.	—	2016	→
Ethnically shared and heterogeneous impacts of molecular pathways suggested by the genome-wide meta-analysis of rheumatoid arthritis.	Okada Y et al.	—	2016	→
Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.	Lamparter D et al.	—	2016	→
Fine-mapping, novel loci identification, and SNP association transferability in a genome-wide association study of QRS duration in African Americans.	Evans DS et al.	—	2016	→
Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases.	Nakka P et al.	—	2016	→
Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.	Eising E et al.	—	2016	→
Gene set analysis for interpreting genetic studies.	Pers TH	—	2016	→
Genetic Moderation of Stress Effects on Corticolimbic Circuitry.	Bogdan R et al.	—	2016	→
Genetics of Coronary Artery Disease.	McPherson R et al.	—	2016	→
Genetic variants near MLST8 and DHX57 affect the epigenetic age of the cerebellum.	Lu AT et al.	—	2016	→
Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci.	Jones SE et al.	—	2016	→
Genome-wide associations for birth weight and correlations with adult disease.	Horikoshi M et al.	—	2016	→
Genome-wide association study of antidepressant response: involvement of the inorganic cation transmembrane transporter activity pathway.	Cocchi E et al.	—	2016	→
Genome-wide association study of biologically informed periodontal complex traits offers novel insights into the genetic basis of periodontal disease.	Offenbacher S et al.	—	2016	→
Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior.	Cornelis MC et al.	—	2016	→
Genome-wide significant results identified for plasma apolipoprotein H levels in middle-aged and older adults.	Mather KA et al.	—	2016	→
GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS.	Hoppmann AS et al.	—	2016	→
GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person.	Hu Y et al.	—	2016	→
Identification of important genes associated with total cholesterol using bioinformatics analysis.	Mo XB et al.	—	2016	→
Integrated pathway analysis of nasopharyngeal carcinoma implicates the axonemal dynein complex in the Malaysian cohort.	Chin YM et al.	—	2016	→
Low-frequency and common genetic variation in ischemic stroke: The METASTROKE collaboration.	Malik R et al.	—	2016	→
Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems.	Shu L et al.	—	2016	→
Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis.	Creanza TM et al.	—	2016	→
Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions.	Hill WD et al.	—	2016	→
Multiple analyses of large-scale genome-wide association study highlight new risk pathways in lumbar spine bone mineral density.	Wei J et al.	—	2016	→
Novel genetic loci underlying human intracranial volume identified through genome-wide association.	Adams HH et al.	—	2016	→
Ovarian Physiology and GWAS: Biobanks, Biology, and Beyond.	Laisk-Podar T et al.	—	2016	→
Post-translational modifications of the cardiac proteome in diabetes and heart failure.	Wende AR	—	2016	→
Protein function in precision medicine: deep understanding with machine learning.	Rost B et al.	—	2016	→
Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function.	Schwarz E et al.	—	2016	→
Shared genetic contribution to Ischaemic Stroke and Alzheimer's Disease.	Traylor M et al.	—	2016	→
Significant impact of miRNA-target gene networks on genetics of human complex traits.	Okada Y et al.	—	2016	→
Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies.	Choi SH et al.	—	2016	→
snpGeneSets: An R Package for Genome-Wide Study Annotation.	Mei H et al.	—	2016	→
The microtubule-associated molecular pathways may be genetically disrupted in patients with Bipolar Disorder. Insights from the molecular cascades.	Drago A et al.	—	2016	→
The statistical properties of gene-set analysis.	de Leeuw CA et al.	—	2016	→
Tracking Cancer Genetic Evolution using OncoTrack.	Talukder AK et al.	—	2016	→
Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension.	Surendran P et al.	—	2016	→
Whole Exome Sequencing in Atrial Fibrillation.	Lubitz SA et al.	—	2016	→
Whole-genome association analysis of treatment response in obsessive-compulsive disorder.	Qin H 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	→
A genome-wide association study identifies four novel susceptibility loci underlying inguinal hernia.	Jorgenson E et al.	—	2015	→
A guide to genome-wide association analysis and post-analytic interrogation.	Reed E et al.	—	2015	→
Biological interpretation of genome-wide association studies using predicted gene functions.	Pers TH et al.	—	2015	→
Computational dissection of human episodic memory reveals mental process-specific genetic profiles.	Luksys G et al.	—	2015	→
Differential Genetic Effects on Statin-Induced Changes Across Low-Density Lipoprotein-Related Measures.	Chu AY et al.	—	2015	→
Discovery of six new susceptibility loci and analysis of pleiotropic effects in leprosy.	Liu H et al.	—	2015	→
Gene set analysis: A step-by-step guide.	Mooney MA 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 basis of autoimmunity.	Marson A et al.	—	2015	→
Genetic studies of body mass index yield new insights for obesity biology.	Locke AE et al.	—	2015	→
Genome-wide Analysis Identifies Novel Loci Associated with Ovarian Cancer Outcomes: Findings from the Ovarian Cancer Association Consortium.	Johnatty SE et al.	—	2015	→
Genome-wide association study identifies SNPs in the MHC class II loci that are associated with self-reported history of whooping cough.	McMahon G et al.	—	2015	→
Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension.	Lu X et al.	—	2015	→
Genome-wide association study of selenium concentrations.	Cornelis MC et al.	—	2015	→
Genome-wide association study of virologic response with efavirenz-containing or abacavir-containing regimens in AIDS clinical trials group protocols.	Lehmann DS et al.	—	2015	→
Genome-wide meta-analyses of plasma renin activity and concentration reveal association with the kininogen 1 and prekallikrein genes.	Lieb W et al.	—	2015	→
Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption.	Coffee and Caffeine Genetics Consortium et al.	—	2015	→
Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.	Himmelstein DS et al.	—	2015	→
Integrated pathway and epistasis analysis reveals interactive effect of genetic variants at TERF1 and AFAP1L2 loci on melanoma risk.	Brossard M et al.	—	2015	→
Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair.	Day FR et al.	—	2015	→
Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.	Wessel J et al.	—	2015	→
MAGMA: generalized gene-set analysis of GWAS data.	de Leeuw CA et al.	—	2015	→
Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci.	He M et al.	—	2015	→
Meta gene set enrichment analyses link miR-137-regulated pathways with schizophrenia risk.	Wright C et al.	—	2015	→
Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis.	Paternoster L et al.	—	2015	→
Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.	Kar SP et al.	—	2015	→
New genetic loci link adipose and insulin biology to body fat distribution.	Shungin D et al.	—	2015	→
New suggestive genetic loci and biological pathways for attention function in adult attention-deficit/hyperactivity disorder.	Alemany S et al.	—	2015	→
Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome.	Shim U et al.	—	2015	→
Pathway analysis of genome-wide association datasets of personality traits.	Kim HN et al.	—	2015	→
Pathway analysis with next-generation sequencing data.	Zhao J et al.	—	2015	→
Pathways targeted by antidiabetes drugs are enriched for multiple genes associated with type 2 diabetes risk.	Segrè AV et al.	—	2015	→
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways.	Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium	—	2015	→
Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies.	Wojcik GL et al.	—	2015	→
Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation.	Soler Artigas M 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	→
The cerebellum ages slowly according to the epigenetic clock.	Horvath S et al.	—	2015	→
XomAnnotate: Analysis of Heterogeneous and Complex Exome- A Step towards Translational Medicine.	Talukder AK et al.	—	2015	→
A meta-analysis of gene expression quantitative trait loci in brain.	Kim Y et al.	—	2014	→
ChIP-Enrich: gene set enrichment testing for ChIP-seq data.	Welch RP et al.	—	2014	→
Chronic periodontitis genome-wide association studies: gene-centric and gene set enrichment analyses.	Rhodin K et al.	—	2014	→
Comprehensive review on lactate metabolism in human health.	Adeva-Andany M et al.	—	2014	→
Converging genetic and functional brain imaging evidence links neuronal excitability to working memory, psychiatric disease, and brain activity.	Heck A et al.	—	2014	→
Defining the role of common variation in the genomic and biological architecture of adult human height.	Wood AR et al.	—	2014	→
From the era of genome analysis to the era of genomic drug discovery: a pioneering example of rheumatoid arthritis.	Okada Y	—	2014	→
Functional and genomic context in pathway analysis of GWAS data.	Mooney MA et al.	—	2014	→
Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.	Arking DE et al.	—	2014	→
Genetics of rheumatoid arthritis contributes to biology and drug discovery.	Okada Y et al.	—	2014	→
Genome-wide analysis of methotrexate pharmacogenomics in rheumatoid arthritis shows multiple novel risk variants and leads for TYMS regulation.	Senapati S et al.	—	2014	→
Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age.	Deelen J et al.	—	2014	→
Genome-wide association study identifies common loci influencing circulating glycated hemoglobin (HbA1c) levels in non-diabetic subjects: the Long Life Family Study (LLFS).	An P et al.	—	2014	→
Genome-wide association study of sexual maturation in males and females highlights a role for body mass and menarche loci in male puberty.	Cousminer DL et al.	—	2014	→
Genome-wide association study of urinary albumin excretion rate in patients with type 1 diabetes.	Sandholm N et al.	—	2014	→
GWAS-based pathway analysis differentiates between fluid and crystallized intelligence.	Christoforou A et al.	—	2014	→
Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise.	Correia C et al.	—	2014	→
Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.	Mäkinen VP et al.	—	2014	→
Laying a solid foundation for Manhattan--'setting the functional basis for the post-GWAS era'.	Zhang X et al.	—	2014	→
Meta-analysis of genome-wide association studies in multiethnic Asians identifies two loci for age-related nuclear cataract.	Liao J et al.	—	2014	→
Natural variation in abiotic stress responsive gene expression and local adaptation to climate in Arabidopsis thaliana.	Lasky JR et al.	—	2014	→
[New genetic determinants of glycemic traits: insights in biological pathways of glucose homeostasis].	Bouatia-Naji N	—	2014	→
No large-effect low-frequency coding variation found for myocardial infarction.	Holmen OL et al.	—	2014	→
Novel genetic associations with serum level metabolites identified by phenotype set enrichment analyses.	Ried JS et al.	—	2014	→
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.	Perry JR et al.	—	2014	→
Pathway analyses implicate glial cells in schizophrenia.	Duncan LE et al.	—	2014	→
Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts.	Shim U et al.	—	2014	→
Pathway-based analysis of GWAs data identifies association of sex determination genes with susceptibility to testicular germ cell tumors.	Koster R et al.	—	2014	→
Protein interaction networks reveal novel autism risk genes within GWAS statistical noise.	Correia C et al.	—	2014	→
Response to 'Predicting the diagnosis of autism spectrum disorder using gene pathway analysis'.	Robinson EB et al.	—	2014	→
Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities.	Chan KH et al.	—	2014	→
The genetic relationship between handedness and neurodevelopmental disorders.	Brandler WM et al.	—	2014	→
The Missing lnc(RNA) between the pancreatic β-cell and diabetes.	Kameswaran V et al.	—	2014	→
Whole exome re-sequencing implicates CCDC38 and cilia structure and function in resistance to smoking related airflow obstruction.	Wain LV et al.	—	2014	→
A genetic deconstruction of neurocognitive traits in schizophrenia and bipolar disorder.	Fernandes CP et al.	—	2013	→
A genome-wide association study of early menopause and the combined impact of identified variants.	Perry JR et al.	—	2013	→
A genome-wide association study of resistance to HIV infection in highly exposed uninfected individuals with hemophilia A.	Lane J et al.	—	2013	→
A meta-analysis of thyroid-related traits reveals novel loci and gender-specific differences in the regulation of thyroid function.	Porcu E et al.	—	2013	→
An association study of TOLL and CARD with leprosy susceptibility in Chinese population.	Liu H et al.	—	2013	→
A systems biology framework identifies molecular underpinnings of coronary heart disease.	Huan T et al.	—	2013	→
ChIP-seq in steatohepatitis and normal liver tissue identifies candidate disease mechanisms related to progression to cancer.	Bysani M et al.	—	2013	→
Common variants in left/right asymmetry genes and pathways are associated with relative hand skill.	Brandler WM et al.	—	2013	→
Discovery and refinement of loci associated with lipid levels.	Willer CJ et al.	—	2013	→
Dysregulation of complement system and CD4+ T cell activation pathways implicated in allergic response.	Couto Alves A et al.	—	2013	→
Gene set of nuclear-encoded mitochondrial regulators is enriched for common inherited variation in obesity.	Knoll N et al.	—	2013	→
Genetic predictors of risk and resilience in psychiatric disorders: a cross-disorder genome-wide association study of functional impairment in major depressive disorder, bipolar disorder, and schizophrenia.	McGrath LM et al.	—	2013	→
Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus.	Lu Y et al.	—	2013	→
Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity.	Cousminer DL et al.	—	2013	→
Genome-wide meta-analysis identifies new susceptibility loci for migraine.	Anttila V et al.	—	2013	→
GSVA: gene set variation analysis for microarray and RNA-seq data.	Hänzelmann S et al.	—	2013	→
Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders.	den Hoed M et al.	—	2013	→
Immune-mediated disease genetics: the shared basis of pathogenesis.	Cotsapas C et al.	—	2013	→
Integrative pathway analysis of a genome-wide association study of (V)O(2max) response to exercise training.	Ghosh S et al.	—	2013	→
Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization.	Bønnelykke K et al.	—	2013	→
Mixed modeling of meta-analysis P-values (MixMAP) suggests multiple novel gene loci for low density lipoprotein cholesterol.	Foulkes AS et al.	—	2013	→
Multiethnic meta-analysis of genome-wide association studies in >100 000 subjects identifies 23 fibrinogen-associated Loci but no strong evidence of a causal association between circulating fibrinogen and cardiovascular disease.	Sabater-Lleal M et al.	—	2013	→
Pleiotropy and pathway analyses of genetic variants associated with both type 2 diabetes and prostate cancer.	Raynor LA et al.	—	2013	→
Preliminary evidence of genetic determinants of adiponectin response to fenofibrate in the Genetics of Lipid Lowering Drugs and Diet Network.	Aslibekyan S et al.	—	2013	→
Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits.	Randall JC et al.	—	2013	→
The chromosome 3q25 genomic region is associated with measures of adiposity in newborns in a multi-ethnic genome-wide association study.	Urbanek M et al.	—	2013	→
The miRNA profile of human pancreatic islets and beta-cells and relationship to type 2 diabetes pathogenesis.	van de Bunt M et al.	—	2013	→
The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue.	Drong AW et al.	—	2013	→
A genome-wide association study of men with symptoms of testicular dysgenesis syndrome and its network biology interpretation.	Dalgaard MD et al.	—	2012	→
Association of TGFβ1 and SMAD4 variants in the etiology of keloid scar in the Malay population.	Emami A 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	→
Common variation in oxidative phosphorylation genes is not a major cause of insulin resistance or type 2 diabetes.	Snogdal LS et al.	—	2012	→
Comprehensive literature review and statistical considerations for GWAS meta-analysis.	Begum F et al.	—	2012	→
Coordinating GWAS results with gene expression in a systems immunologic paradigm in autoimmunity.	Stranger BE et al.	—	2012	→
Discovery and fine mapping of serum protein loci through transethnic meta-analysis.	Franceschini N et al.	—	2012	→
Gene-based analysis of regionally enriched cortical genes in GWAS data sets of cognitive traits and psychiatric disorders.	Ersland KM et al.	—	2012	→
Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes.	Morán I et al.	—	2012	→
Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems.	Mercader JM et al.	—	2012	→
Integration of biological networks and pathways with genetic association studies.	Sun YV	—	2012	→
Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.	Chasman DI et al.	—	2012	→
Interpreting noncoding genetic variation in complex traits and human disease.	Ward LD et al.	—	2012	→
JAK and STAT signaling molecules in immunoregulation and immune-mediated disease.	O'Shea JJ et al.	—	2012	→
Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes.	Morris AP et al.	—	2012	→
Linkage-disequilibrium-based binning affects the interpretation of GWASs.	Christoforou A et al.	—	2012	→
Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways.	Stolk L et al.	—	2012	→
Multi-locus genome-wide association analysis supports the role of glutamatergic synaptic transmission in the etiology of major depressive disorder.	Lee PH et al.	—	2012	→
Pathway analysis of genomic data: concepts, methods, and prospects for future development.	Ramanan VK et al.	—	2012	→
Pathway analysis of smoking quantity in multiple GWAS identifies cholinergic and sensory pathways.	Harari O et al.	—	2012	→
Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets.	de las Fuentes L et al.	—	2012	→
PSEA: Phenotype Set Enrichment Analysis--a new method for analysis of multiple phenotypes.	Ried JS et al.	—	2012	→
Synthesizing genome-wide association studies and expression microarray reveals novel genes that act in the human growth plate to modulate height.	Lui JC et al.	—	2012	→
Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.	Schaid DJ et al.	—	2012	→
Boosting signal-to-noise in complex biology: prior knowledge is power.	Ideker T et al.	—	2011	→
Eight common genetic variants associated with serum DHEAS levels suggest a key role in ageing mechanisms.	Zhai G et al.	—	2011	→
From SNPs to genes: disease association at the gene level.	Lehne B et al.	—	2011	→
Gene set analysis of genome-wide association studies: methodological issues and perspectives.	Wang L et al.	—	2011	→
Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function.	Soler Artigas M et al.	—	2011	→
Genome-wide association study in German patients with attention deficit/hyperactivity disorder.	Hinney A et al.	—	2011	→
Integrative genomics strategies to elucidate the complexity of drug response.	Kasarskis A et al.	—	2011	→
Protein interaction-based genome-wide analysis of incident coronary heart disease.	Jensen MK et al.	—	2011	→
The Lin28/let-7 axis regulates glucose metabolism.	Zhu H et al.	—	2011	→
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.	Speliotes EK et al.	—	2010	→
Common genetic variants and modification of penetrance of BRCA2-associated breast cancer.	Gaudet MM et al.	—	2010	→
Hundreds of variants clustered in genomic loci and biological pathways affect human height.	Lango Allen H et al.	—	2010	→
Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.	Elks CE et al.	—	2010	→