Kernel machine regression in neuroimaging genetics

[1]  Seunggeun Lee,et al.  Test for rare variants by environment interactions in sequencing association studies , 2016, Biometrics.

[2]  Weihua Guan,et al.  Sequence Kernel Association Analysis of Rare Variant Set Based on the Marginal Regression Model for Binary Traits , 2015, Genetic epidemiology.

[3]  Stephen R. Williams,et al.  A Fast Multiple‐Kernel Method With Applications to Detect Gene‐Environment Interaction , 2015, Genetic epidemiology.

[4]  W. Pan,et al.  A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. , 2015, American journal of human genetics.

[5]  K Alaine Broadaway,et al.  Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits , 2015, Genetic epidemiology.

[6]  Mert R. Sabuncu,et al.  A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application , 2015, NeuroImage.

[7]  Thomas E. Nichols,et al.  Massively expedited genome-wide heritability analysis (MEGHA) , 2015, Proceedings of the National Academy of Sciences.

[8]  M. Epstein,et al.  Flexible and Robust Methods for Rare‐Variant Testing of Quantitative Traits in Trios and Nuclear Families , 2014, Genetic epidemiology.

[9]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[10]  G. Abecasis,et al.  Rare-variant association analysis: study designs and statistical tests. , 2014, American journal of human genetics.

[11]  Peter M Visscher,et al.  Large-scale genomics unveils the genetic architecture of psychiatric disorders , 2014, Nature Neuroscience.

[12]  Xiaotong Shen,et al.  A Powerful and Adaptive Association Test for Rare Variants , 2014, Genetics.

[13]  Thomas Lumley,et al.  Sequence Kernel Association Test for Survival Traits , 2014, Genetic epidemiology.

[14]  Xihong Lin,et al.  JOINT ANALYSIS OF SNP AND GENE EXPRESSION DATA IN GENETIC ASSOCIATION STUDIES OF COMPLEX DISEASES. , 2014, The annals of applied statistics.

[15]  Mert R. Sabuncu,et al.  Spatiotemporal Linear Mixed Effects Modeling for the Mass-univariate Analysis of Longitudinal Neuroimage Data ⁎ for the Alzheimer's Disease Neuroimaging Initiative 1 , 2022 .

[16]  Paul M. Thompson,et al.  Genetics of the connectome , 2013, NeuroImage.

[17]  Xihong Lin,et al.  Test for interactions between a genetic marker set and environment in generalized linear models. , 2013, Biostatistics.

[18]  Seunggeun Lee,et al.  General framework for meta-analysis of rare variants in sequencing association studies. , 2013, American journal of human genetics.

[19]  Iuliana Ionita-Laza,et al.  Sequence kernel association tests for the combined effect of rare and common variants. , 2013, American journal of human genetics.

[20]  Iuliana Ionita-Laza,et al.  Family-based association tests for sequence data, and comparisons with population-based association tests , 2013, European Journal of Human Genetics.

[21]  Mert R. Sabuncu,et al.  Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models , 2013, NeuroImage.

[22]  J. Meigs,et al.  Sequence Kernel Association Test for Quantitative Traits in Family Samples , 2013, Genetic epidemiology.

[23]  Min A. Jhun,et al.  SNP Set Association Analysis for Familial Data , 2012, Genetic epidemiology.

[24]  Paul M. Thompson,et al.  Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures , 2012, NeuroImage.

[25]  Arnab Maity,et al.  Multivariate Phenotype Association Analysis by Marker‐Set Kernel Machine Regression , 2012, Genetic epidemiology.

[26]  Tianxi Cai,et al.  Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test. , 2012, Biostatistics.

[27]  Xihong Lin,et al.  Optimal tests for rare variant effects in sequencing association studies. , 2012, Biostatistics.

[28]  Yuehua Cui,et al.  Gene-centric gene–gene interaction: A model-based kernel machine method , 2012, 1209.6502.

[29]  M. Rieder,et al.  Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. , 2012, American journal of human genetics.

[30]  Michael Weiner,et al.  Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[31]  Michael W. Weiner,et al.  Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression , 2012, Front. Neurosci..

[32]  P. Visscher,et al.  Five years of GWAS discovery. , 2012, American journal of human genetics.

[33]  Xihong Lin,et al.  Powerful Tests for Detecting a Gene Effect in the Presence of Possible Gene–Gene Interactions Using Garrote Kernel Machines , 2011, Biometrics.

[34]  Xihong Lin,et al.  Kernel machine SNP‐set analysis for censored survival outcomes in genome‐wide association studies , 2011, Genetic epidemiology.

[35]  Tianxi Cai,et al.  Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction , 2011, Biometrics.

[36]  Xihong Lin,et al.  Rare-variant association testing for sequencing data with the sequence kernel association test. , 2011, American journal of human genetics.

[37]  Michael Weiner,et al.  Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects , 2011, NeuroImage.

[38]  Michael Weiner,et al.  Boosting Power to Detect Genetic Associations in Imaging Using Multi-locus, Genome-wide Scans and Ridge Regression , 2022 .

[39]  Chiou-Shann Fuh,et al.  Multiple Kernel Learning for Dimensionality Reduction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Wei Pan,et al.  Relationship between genomic distance‐based regression and kernel machine regression for multi‐marker association testing , 2011, Genetic epidemiology.

[41]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[42]  Andrew J. Saykin,et al.  Voxelwise genome-wide association study (vGWAS) , 2010, NeuroImage.

[43]  Daniel J Schaid,et al.  Genomic Similarity and Kernel Methods II: Methods for Genomic Information , 2010, Human Heredity.

[44]  P. Visscher,et al.  Common SNPs explain a large proportion of the heritability for human height , 2010, Nature Genetics.

[45]  Daniel J Schaid,et al.  Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and Statistical Foundations , 2010, Human Heredity.

[46]  Deanne M. Taylor,et al.  Powerful SNP-set analysis for case-control genome-wide association studies. , 2010, American journal of human genetics.

[47]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[48]  E. Zeggini,et al.  An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies , 2009, Genetic epidemiology.

[49]  Wei Pan,et al.  Asymptotic tests of association with multiple SNPs in linkage disequilibrium , 2009, Genetic epidemiology.

[50]  S. Browning,et al.  A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic , 2009, PLoS genetics.

[51]  S. Leal,et al.  Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. , 2008, American journal of human genetics.

[52]  Dawei Liu,et al.  Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models , 2008, BMC Bioinformatics.

[53]  D. Gianola,et al.  Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits , 2008, Genetics.

[54]  Xihong Lin,et al.  A powerful and flexible multilocus association test for quantitative traits. , 2008, American journal of human genetics.

[55]  Xihong Lin,et al.  Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models , 2007, Biometrics.

[56]  W. Thilly,et al.  A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). , 2007, Mutation research.

[57]  B. Schölkopf,et al.  Kernel methods in machine learning , 2007, math/0701907.

[58]  N. Schork,et al.  Generalized genomic distance-based regression methodology for multilocus association analysis. , 2006, American journal of human genetics.

[59]  A. Meyer-Lindenberg,et al.  Intermediate phenotypes and genetic mechanisms of psychiatric disorders , 2006, Nature Reviews Neuroscience.

[60]  Sara van de Geer,et al.  Testing against a high dimensional alternative , 2006 .

[61]  Charles E. McCulloch,et al.  Generalized Linear Mixed Models , 2005 .

[62]  Chong Gu Smoothing Spline Anova Models , 2002 .

[63]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[64]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[65]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[66]  D. Kuonen Saddlepoint approximations for distributions of quadratic forms in normal variables , 1999 .

[67]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[68]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[69]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[70]  M. Kenward,et al.  Small sample inference for fixed effects from restricted maximum likelihood. , 1997, Biometrics.

[71]  Xihong Lin Variance component testing in generalised linear models with random effects , 1997 .

[72]  P. Diggle Analysis of Longitudinal Data , 1995 .

[73]  Robin Thompson,et al.  Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models , 1995 .

[74]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[75]  G. Wahba Spline Models for Observational Data , 1990 .

[76]  D. Bates,et al.  Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data , 1988 .

[77]  K. Liang,et al.  Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions , 1987 .

[78]  N. Laird,et al.  Maximum likelihood computations with repeated measures: application of the EM algorithm , 1987 .

[79]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[80]  R. Davies The distribution of a linear combination of 2 random variables , 1980 .

[81]  D. Harville Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems , 1977 .

[82]  H. D. Patterson,et al.  Recovery of inter-block information when block sizes are unequal , 1971 .

[83]  J. Gower Some distance properties of latent root and vector methods used in multivariate analysis , 1966 .

[84]  N. Aronszajn Theory of Reproducing Kernels. , 1950 .

[85]  J. Mercer Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[86]  Tanja Hueber,et al.  Gaussian Processes For Machine Learning , 2016 .

[87]  Xihong Lin,et al.  Hypothesis testing in semiparametric additive mixed models. , 2003, Biostatistics.

[88]  Brian H. McArdle,et al.  FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE‐BASED REDUNDANCY ANALYSIS , 2001 .

[89]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[90]  Saburou Saitoh,et al.  Theory of Reproducing Kernels and Its Applications , 1988 .

[91]  G. Wahba,et al.  Some results on Tchebycheffian spline functions , 1971 .