TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies.
- Authors
- van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V
- Year
- 2013
- Journal
- PLoS genetics
- PMID
- 23359524
- DOI
- 10.1371/journal.pgen.1003235
- PMCID
- PMC3554627
To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype-phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype-phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5-9 times higher than the power of univariate tests based on composite scores and 1.5-2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype-phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.
Schematic representation of the simulation settings and results.Schematic representation of the simulation settings (aβf) and radar plot (g) of the power to detect 1 genetic variant (GV) explaining .5% of the phenotypic variance in 12 simulation settings. The power radar plot (power running from 0 (midpoint) to 1 (outer edge)) displays the power for the univariate sum score analyses (blue), MANOVA (green), and TATES (red). The phenotypic correlation structure was either due to one common factor (a,e), multiple underlying latent factors (b,c,d), or a network model (f). Within these phenotypic settings, the GV either affected multiple phenotypes via a common factor (a,b,c), or affect a single phenotype directly (d,e,f). Power results for 12 simulation settings and a GV explaining .5% of the variance are highlighted (g, colour labels corresponds to colour simulation settings; see Tables S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12 for more GV effect sizes). Specifically, gA1β3: 1-factor models with GV effect on the factor. gA1: mix of dichotomous, ordinal and continuous phenotypes correlating .36 to .81; gA2: continuous phenotypes correlating .56; gA3: continuous phenotypes correlating .12. gE1β3: 1-factor models with GV effect specific to 1 phenotype. gE1: phenotypes correlate .56 (like gA2); gE2: phenotypes correlate .30; gE3, phenotypes correlate .12 (like gA3). gF1βF3: network models with GV effect specific to 1 phenotype. gF1: phenotypes correlate .56 (like gA2 and gE1); gF2: phenotypes correlate .12 (like gA3 and gE3); gF3: 4 clusters of phenotypes that within clusters correlate .55, and between clusters correlate .13. gC1: 2-factor model, 10 phenotypes per factor, correlating .36β.81 within factors, and a factorial correlation of .5. GV affects only the 2nd factor. gB1: 4-factor model, 5 phenotypes per factor, correlating .81 within factors, and factorial correlations of .1. GV affects only the 4th factor. gD1: like gB1 but GV effect specific to 1 phenotype.
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| Citation | PMID | DOI | Status |
|---|---|---|---|
| AbecasisGR, ChernySS, CooksonWO, CardonLR (2002) Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30: 97β101.1173179710.1038/ng786 | β | β | β |
| Achenbach TM (1991) Manual for the Child Behavior Checklist/4β18. Burlington, VT: University of Vermont, Department of Psychiatry. | β | β | β |
| AulchenkoYS, RipkeS, IsaacsA, van DuijnCM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23: 1294β1296.1738401510.1093/bioinformatics/btm108 | β | β | β |
| AulchenkoYS, StruchalinMV, van DuijnCM (2010) ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 11: 134.2023339210.1186/1471-2105-11-134PMC2846909 | β | β | β |
| BlossCS, SchiaborKM, SchorkNJ (2010) Human behavioral informatics in genetic studies of neuropsychiatric disease: multi-variate profile-based analysis. Brain Res Bull 83: 177β188.2043390710.1016/j.brainresbull.2010.04.012PMC2941546 | β | β | β |
| BorsboomD, CramerAOJ, SchmittmannVD, EpskampS, WaldorpLJ (2011) The small world of psychopathology. Plos One 6: e27407.2211467110.1371/journal.pone.0027407PMC3219664 | β | β | β |
| BrzustowicsLM, BassettAS (2008) Phenotype matters: The case for careful characterization of relevant traits. Am J Psychiat 165: 1096β1098.1876548910.1176/appi.ajp.2008.08060897PMC3276589 | β | β | β |
| Carroll JB (1993) Human Cognitive abilities: A survey of factor analytic studies. Cambridge University press. | β | β | β |
| ColeDA, MaxwellSE, AvreyRD, SalasE (1994) How the power of MANOVA can both increase and decrease as a function of the intercorrelations among the dependent variables. Psychol Bull 115: 465β474. | β | β | β |
| CorvinA, CraddockN, SullivanPF (2010) Genome-wide association studies: a primer. Psychol Med 40: 1063β1077.1989572210.1017/S0033291709991723PMC4181332 | β | β | β |
| CramerAOJ, WaldorpLJ, van der MaasHLJ, BorsboomD (2010) Comorbidity: a network perspective. Behav Brain Sci 33: 137β193.2058436910.1017/S0140525X09991567 | β | β | β |
| DigmanJM (1997) Higher-order factors of the big five. J Pers Soc Psychol 73: 1246β1256.941827810.1037//0022-3514.73.6.1246 | β | β | β |
| DowellRD, RyanO, JansenA, CheungD, AgarwalaS, et al (2010) Genotype to phenotype: a complex problem. Science 328: 469β469.2041349310.1126/science.1189015PMC4412269 | β | β | β |
| FerreiraMAR, PurcellSM (2009) A multivariate test of association. Bioinformatics 25: 132β133.1901984910.1093/bioinformatics/btn563 | β | β | β |
| HendersonND, TurriMG, DeFriesJC, FlintJ (2004) QTL analysis of multiple behavioural measures of anxiety in mice. Behav Genet 34: 267β293.1499086710.1023/B:BEGE.0000017872.25069.44 | β | β | β |
| HouleD, GovindarajuDR, OmholtS (2010) Phenomics: the next challenge. Nat Rev Genet 11: 855β866.2108520410.1038/nrg2897 | β | β | β |
| JΓΆreskog KG, SΓΆrbom D (1996β2001) LISREL 8 User's Reference Guide, SSI Scientific Software International. Suite. USA | β | β | β |
| LangeC, DeMeoD, SilvermanEK, WeissST, LairdNM (2004) PBAT: Tools for family-based association studies. Am J Hum Genet 74: 367β369.1474032210.1086/381563PMC1181934 | β | β | β |
| Lawley DN, Maxwell AE (1971) Factor analysis as a statistical method. London: Butterworth. | β | β | β |
| LiM-X, GuiH-S, KwanJSH, ShamPC (2011) GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet 88: 283β293.2139706010.1016/j.ajhg.2011.01.019PMC3059433 | β | β | β |
| LiY, WillerCJ, DingJ, ScheetP, AbecasisGR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816β834.2105833410.1002/gepi.20533PMC3175618 | β | β | β |
| LiY, WillerCJ, SannaS, AbecasisGR (2009) Genotype Imputation. Annu Rev Genomics Hum Genet 10: 387β406.1971544010.1146/annurev.genom.9.081307.164242PMC2925172 | β | β | β |
| MarchiniJ, HowieB, MyersS, McVeanG, DonnellyP (2007) A new multipoint method for genome-wide association studies via imputation of genotypes. Nat Genet 39: 906β913.1757267310.1038/ng2088 | β | β | β |
| McClellanJ, KingMC (2010) Genetic heterogeneity in human disease. Cell 141: 210β217.2040331510.1016/j.cell.2010.03.032 | β | β | β |
| MedlandS, NealeMC (2010) An integrated phenomic approach to multivariate allelic association. Eur J Hum Genet 18: 233β239.1970724610.1038/ejhg.2009.133PMC2807471 | β | β | β |
| MinicaCC, BoomsmaDI, van der SluisS, DolanCV (2010) Genetic Association in Multivariate Phenotypic Data: Power in Five Models. Twin Res Hum Genet 13: 525β543.2114292910.1375/twin.13.6.525 | β | β | β |
| MuthΓ©n LK, MuthΓ©n BO (1998β2012) Mplus User's Guide. Seventh Edition. Los Angeles, CA: MuthΓ©n & MuthΓ©n | β | β | β |
| Neale MC, Boker SM, Xie G, Maes HH (2006) Mx: statistical modeling, 7th edn. Department of Psychiatry, VCU, Richmond. | β | β | β |
| O'ReillyPF, HoggartCJ, PomyenY, CalboliFCF, ElliottP, et al (2012) MultiPhen: Joint model of multiple phenotypes can increase discovery in GWAS. Plos One 7: e34861.2256709210.1371/journal.pone.0034861PMC3342314 | β | β | β |
| PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMAR, et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559β575.1770190110.1086/519795PMC1950838 | β | β | β |
| R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. | β | β | β |
| Rasch G (1980) Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press. | β | β | β |
| Van der MaasHLJ, DolanCV, GrasmanRPPP, WichertsJM, HuizengaHM, et al (2006) A Dynamic model of general intelligence: the positive manifold of intelligence by mutualism. Psychol Rev 113: 842β861.1701430510.1037/0033-295X.113.4.842 | β | β | β |
| Van der SluisS, PosthumaD, NivardMG, VerhageM, DolanCV (2012) Power in GWAS: lifting the curse of the clinical cut-off. Mol Psych doi:10.1038/mp.2012.65.10.1038/mp.2012.6522614290 | β | β | β |
| Van der SluisS, VerhageM, PosthumaD, DolanCV (2010) Phenotypic Complexity, Measurement Bias, and Poor Phenotypic Resolution Contribute to the Missing Heritability Problem in Genetic Association Studies. Plos One 5: e13929.2108566610.1371/journal.pone.0013929PMC2978099 | β | β | β |
| WhitlockMC (2005) Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J Evolution Biol 18: 1368β1373.10.1111/j.1420-9101.2005.00917.x16135132 | β | β | β |
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| The genetic architecture of human cerebellar morphology supports a key role for the cerebellum in human evolution and psychopathology. | Moberget T et al. | β | 2026 | β |
| Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study. | Wan NC et al. | β | 2025 | β |
| Network construction using sparse Gaussian graphical model based on GWAS summary statistics. | Subedi M et al. | β | 2025 | β |
| Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers. | van der Meer D et al. | β | 2025 | β |
| An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. | Deng Q et al. | β | 2024 | β |
| A novel phenotype imputation method with copula model. | Zhang J et al. | β | 2024 | β |
| GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction. | Liu X et al. | β | 2024 | β |
| Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions. | Blancon J et al. | β | 2024 | β |
| Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review. | Sun L et al. | β | 2024 | β |
| MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test. | Guo H et al. | β | 2024 | β |
| Multiple phenotype association tests based on sliced inverse regression. | Sun W et al. | β | 2024 | β |
| Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer. | Bouttle K et al. | β | 2024 | β |
| Adaptively Integrative Association between Multivariate Phenotypes and Transcriptomic Data for Complex Diseases. | Li Y et al. | β | 2023 | β |
| graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. | Deng Q et al. | β | 2023 | β |
| Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis. | Liu X et al. | β | 2023 | β |
| Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network. | Xie H et al. | β | 2023 | β |
| Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks. | Zigarelli AM et al. | β | 2023 | β |
| Multivariate genome-wide associations for immune traits in two maternal pig lines. | Roth K et al. | β | 2023 | β |
| Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection. | Guo H et al. | β | 2023 | β |
| Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants. | Bu D et al. | β | 2023 | β |
| Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank. | Zhang Y et al. | β | 2023 | β |
| A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. | Wang M et al. | β | 2022 | β |
| A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits. | Fernandes SB et al. | β | 2022 | β |
| An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes. | Wen Y et al. | β | 2022 | β |
| A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC. | Fu L et al. | β | 2022 | β |
| Gene-based association tests using GWAS summary statistics and incorporating eQTL. | Cao X et al. | β | 2022 | β |
| HCLC-FC: A novel statistical method for phenome-wide association studies. | Liang X et al. | β | 2022 | β |
| Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing. | Wang T et al. | β | 2022 | β |
| Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders. | Zhang X et al. | β | 2022 | β |
| Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci. | Lyu C et al. | β | 2022 | β |
| Strong and weak cross-inheritance of substance use disorders in a nationally representative sample. | Zhang H et al. | β | 2022 | β |
| The role of critical immune genes in brain disorders: insights from neuroimaging immunogenetics. | Bian B et al. | β | 2022 | β |
| AGNEP: An Agglomerative Nesting Clustering Algorithm for Phenotypic Dimension Reduction in Joint Analysis of Multiple Phenotypes. | Liu F et al. | β | 2021 | β |
| A Novel Approach Integrating Hierarchical Clustering and Weighted Combination for Association Study of Multiple Phenotypes and a Genetic Variant. | Fu L et al. | β | 2021 | β |
| Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies. | Chien LC | β | 2021 | β |
| Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology. | Gialluisi A et al. | β | 2021 | β |
| Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data. | Warrington NM et al. | β | 2021 | β |
| GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS. | Pritikin JN et al. | β | 2021 | β |
| Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. | Li R et al. | β | 2021 | β |
| MF-TOWmuT: Testing an optimally weighted combination of common and rare variants with multiple traits using family data. | Gao C et al. | β | 2021 | β |
| Multitrait GWAS to connect disease variants and biological mechanisms. | Julienne H et al. | β | 2021 | β |
| Nontrivial Replication of Loci Detected by Multi-Trait Methods. | Ning Z et al. | β | 2021 | β |
| Notes on Three Decades of Methodology Workshops. | Maes HH | β | 2021 | β |
| The genetic architecture of human cortical folding. | van der Meer D et al. | β | 2021 | β |
| Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks. | Wolf JM et al. | β | 2020 | β |
| Discovery of shared genomic loci using the conditional false discovery rate approach. | Smeland OB et al. | β | 2020 | β |
| Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat. | Tsai HY et al. | β | 2020 | β |
| JASS: command line and web interface for the joint analysis of GWAS results. | Julienne H et al. | β | 2020 | β |
| Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering. | Li X et al. | β | 2020 | β |
| Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS. | Xue H et al. | β | 2020 | β |
| MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics. | Lin J et al. | β | 2020 | β |
| Powerful rare variant association testing in a copula-based joint analysis of multiple phenotypes. | Konigorski S et al. | β | 2020 | β |
| The genetic architecture of the human cerebral cortex. | Grasby KL et al. | β | 2020 | β |
| Truncated tests for combining evidence of summary statistics. | Bu D et al. | β | 2020 | β |
| Understanding the genetic determinants of the brain with MOSTest. | van der Meer D et al. | β | 2020 | β |
| A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. | Sha Q et al. | β | 2019 | β |
| A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies. | Liu Z et al. | β | 2019 | β |
| A Multivariate Genome-Wide Association Study of Wing Shape in <i>Drosophila melanogaster</i>. | Pitchers W et al. | β | 2019 | β |
| Comparison of <i>F</i>-tests for Univariate and Multivariate Mixed-Effect Models in Genome-Wide Association Mapping. | Onogi A | β | 2019 | β |
| Comparison of methods for multivariate gene-based association tests for complex diseases using common variants. | Chung J et al. | β | 2019 | β |
| Genetic correlations of polygenic disease traits: from theory to practice. | van Rheenen W et al. | β | 2019 | β |
| Genome-wide association scan identifies new variants associated with a cognitive predictor of dyslexia. | Gialluisi A et al. | β | 2019 | β |
| Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. | Grotzinger AD et al. | β | 2019 | β |
| Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error. | Yang X et al. | β | 2019 | β |
| Multivariate genome-wide analyses of the well-being spectrum. | Baselmans BML et al. | β | 2019 | β |
| Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy. | Nath AP et al. | β | 2019 | β |
| Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data. | Masotti M et al. | β | 2019 | β |
| Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data. | Guo B et al. | β | 2019 | β |
| Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies. | Holleman AM et al. | β | 2019 | β |
| Whole Exome Sequencing Study of Parkinson Disease and Related Endophenotypes in the Italian Population. | Gialluisi A et al. | β | 2019 | β |
| A Brief Critique of the TATES Procedure. | Aliev F et al. | β | 2018 | β |
| A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes. | Liang X et al. | β | 2018 | β |
| A novel method to test associations between a weighted combination of phenotypes and genetic variants. | Zhu H et al. | β | 2018 | β |
| Association analysis of multiple traits by an approach of combining P values. | Chen L et al. | β | 2018 | β |
| Association analysis of rare and common variants with multiple traits based on variable reduction method. | Chen L et al. | β | 2018 | β |
| A univariate perspective of multivariate genome-wide association analysis. | Guo X et al. | β | 2018 | β |
| Contribution of genes in the GABAergic pathway to bipolar disorder and its executive function deficit in the Chinese Han population. | Ren H et al. | β | 2018 | β |
| Deviations from Expectations: A Commentary on Aliev et al. | van der Sluis S et al. | β | 2018 | β |
| Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits. | Wu B et al. | β | 2018 | β |
| Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions. | Liang X et al. | β | 2018 | β |
| Methods and results from the genome-wide association group at GAW20. | Wang X et al. | β | 2018 | β |
| Multivariate Methods for Meta-Analysis of Genetic Association Studies. | Dimou NL et al. | β | 2018 | β |
| Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. | Zeng P et al. | β | 2018 | β |
| Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy. | Salinas YD et al. | β | 2018 | β |
| Testing an optimally weighted combination of common and/or rare variants with multiple traits. | Wang Z et al. | β | 2018 | β |
| Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations. | Kim J et al. | β | 2017 | β |
| APPROXIMATING PRINCIPAL GENETIC COMPONENTS OF SUBCORTICAL SHAPE. | Gutman BA et al. | β | 2017 | β |
| A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data. | Lin N et al. | β | 2017 | β |
| A small-sample multivariate kernel machine test for microbiome association studies. | Zhan X et al. | β | 2017 | β |
| Dimensionality and Genetic Correlates of Problem Behavior in Low-Income African American Adolescents. | Latendresse SJ et al. | β | 2017 | β |
| GATE: an efficient procedure in study of pleiotropic genetic associations. | Zhang W et al. | β | 2017 | β |
| Genetics of pleiotropic effects of dexamethasone. | Ramsey LB et al. | β | 2017 | β |
| Genome-wide association test of multiple continuous traits using imputed SNPs. | Wu B et al. | β | 2017 | β |
| GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling. | Verhulst B et al. | β | 2017 | β |
| Heritability of Behavioral Problems in 7-Year Olds Based on Shared and Unique Aspects of Parental Views. | Fedko IO et al. | β | 2017 | β |
| Multivariate simulation framework reveals performance of multi-trait GWAS methods. | Porter HF et al. | β | 2017 | β |
| Neuroimaging genetic analyses of novel candidate genes associated with reading and language. | Gialluisi A et al. | β | 2017 | β |
| Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. | Karnes JH et al. | β | 2017 | β |
| Phenotype validation in electronic health records based genetic association studies. | Wang L et al. | β | 2017 | β |
| Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits. | Zhan X et al. | β | 2017 | β |
| Rare variant association test with multiple phenotypes. | Lee S et al. | β | 2017 | β |
| Selecting cases and controls for DNA sequencing studies using family histories of disease. | Kim W et al. | β | 2017 | β |
| Statistical methods to detect pleiotropy in human complex traits. | Hackinger S et al. | β | 2017 | β |
| The common variants implicated in microstructural abnormality of first episode and drug-naΓ―ve patients with schizophrenia. | Ren HY et al. | β | 2017 | β |
| Accelerating Gene Discovery by Phenotyping Whole-Genome Sequenced Multi-mutation Strains and Using the Sequence Kernel Association Test (SKAT). | Timbers TA et al. | β | 2016 | β |
| An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies. | Liang X et al. | β | 2016 | β |
| An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function. | Yang JJ et al. | β | 2016 | β |
| Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis. | Wang L et al. | β | 2016 | β |
| Finding structure in data using multivariate tree boosting. | Miller PJ et al. | β | 2016 | β |
| Joint Analysis of Multiple Traits in Rare Variant Association Studies. | Wang Z et al. | β | 2016 | β |
| Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test. | Wang Z et al. | β | 2016 | β |
| metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. | Cichonska A et al. | β | 2016 | β |
| Multivariate Gene-Based Association Test on Family Data in MGAS. | Vroom CR et al. | β | 2016 | β |
| Phenome-wide association study maps new diseases to the human major histocompatibility complex region. | Liu J et al. | β | 2016 | β |
| Pleiotropic Meta-Analyses of Longitudinal Studies Discover Novel Genetic Variants Associated with Age-Related Diseases. | He L et al. | β | 2016 | β |
| Sequence Kernel Association Test of Multiple Continuous Phenotypes. | Wu B et al. | β | 2016 | β |
| Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014. | Aas M et al. | β | 2016 | β |
| The CHRM3 gene is implicated in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia. | Wang Q et al. | β | 2016 | β |
| Tree-based quantitative trait mapping in the presence of external covariates. | Thompson KL et al. | β | 2016 | β |
| Unravelling the human genome-phenome relationship using phenome-wide association studies. | Bush WS et al. | β | 2016 | β |
| USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis. | Ray D et al. | β | 2016 | β |
| An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics. | Kim J et al. | β | 2015 | β |
| An alternative approach to multiple testing for methylation QTL mapping reduces the proportion of falsely identified CpGs. | Luijk R et al. | β | 2015 | β |
| Common genetic variants influence human subcortical brain structures. | Hibar DP et al. | β | 2015 | β |
| Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants. | Won S et al. | β | 2015 | β |
| Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease. | Yan J et al. | β | 2015 | β |
| Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. | Zhu X et al. | β | 2015 | β |
| MGAS: a powerful tool for multivariate gene-based genome-wide association analysis. | Van der Sluis S et al. | β | 2015 | β |
| Pathway-based association study of multiple candidate genes and multiple traits using structural equation models. | Romdhani H et al. | β | 2015 | β |
| Phenome-Wide Association Studies: Embracing Complexity for Discovery. | Pendergrass SA et al. | β | 2015 | β |
| Power Comparisons of Methods for Joint Association Analysis of Multiple Phenotypes. | Zhu H et al. | β | 2015 | β |
| Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning. | Padhukasahasram B et al. | β | 2015 | β |
| Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders. | Xu MK et al. | β | 2015 | β |
| Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance. | Majumdar A et al. | β | 2015 | β |
| Statistical methods for association tests of multiple continuous traits in genome-wide association studies. | Wu B et al. | β | 2015 | β |
| The effects of chromatin organization on variation in mutation rates in the genome. | Makova KD et al. | β | 2015 | β |
| A comparison of multivariate genome-wide association methods. | Galesloot TE et al. | β | 2014 | β |
| Genetic and environmental stability of intelligence in childhood and adolescence. | FraniΔ S et al. | β | 2014 | β |
| Genetics of the human metabolome, what is next? | Dharuri H et al. | β | 2014 | β |
| Genome-wide analyses of borderline personality features. | Lubke GH et al. | β | 2014 | β |
| High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software | Fabregat-Traver D et al. | β | 2014 | β |
| High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software. | Fabregat-Traver D et al. | β | 2014 | β |
| Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. | Aschard H et al. | β | 2014 | β |
| Multivariate genetic analyses in heterogeneous populations. | Lubke G et al. | β | 2014 | β |
| Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data. | Zhang Y et al. | β | 2014 | β |
| A general framework for association tests with multivariate traits in large-scale genomics studies. | He Q et al. | β | 2013 | β |
| A rapid gene-based genome-wide association test with multivariate traits. | Basu S et al. | β | 2013 | β |
| Pleiotropy in complex traits: challenges and strategies. | Solovieff N et al. | β | 2013 | β |
| Unraveling genetic origin of aging-related traits: evolving concepts. | Kulminski AM | β | 2013 | β |