Cited by 227Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.

A Powerful and Flexible Multilocus Association Test for Quantitative Traits

Open access

Kwee, Lydia Coulter, Liu, Dawei, Lin, Xihong, Ghosh, Debashis, Epstein, Michael P.·2008·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2007.10.010PMC PMC2664991

Open this paper in CoChat — free account Open PDF

Abstract

Association mapping of complex traits typically employs tagSNP genotype data to identify a trait locus within a region of interest. However, considerable debate exists regarding the most powerful strategy for utilizing such tagSNP data for inference. A popular approach tests each tagSNP within the region individually, but such tests could lose power as a result of incomplete linkage disequilibrium between the genotyped tagSNP and the trait locus. Alternatively, one can jointly test all tagSNPs simultaneously within the region (by using genotypes or haplotypes), but such multivariate tests have large degrees of freedom that can also compromise power. Here, we consider a semiparametric model for quantitative-trait mapping that uses genetic information from multiple tagSNPs simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared to existing multivariate approaches. We fit this model by using a dimension-reducing technique called least-squares kernel machines, which we show is identical to analysis using a specific linear mixed model (which we can fit by using standard software packages like SAS and R). Using simulated SNP data based on real data from the International HapMap Project, we demonstrate that our approach often has superior performance for association mapping of quantitative traits compared to the popular approach of single-tagSNP testing. Our approach is also flexible, because it allows easy modeling of covariates and, if interest exists, high-dimensional interactions among tagSNPs and environmental predictors.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.ajhg.2007.10.010

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
A New Statistical Method for Haplotype Reconstruction from Population Data
Stephens, M., Smith, N. J. C., & Donnelly, P. (2001) · The American Journal of Human Genetics · doi:10.1086/319501 · Cited by 7,512
A haplotype map of the human genome
The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,359
Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees
Almasy, L. A., & Blangero, J. (1998) · The American Journal of Human Genetics · doi:10.1086/301844 · Cited by 3,021
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase
Scheet, P. A., & Stephens, M. (2006) · The American Journal of Human Genetics · doi:10.1086/502802 · Cited by 1,959
Score Tests for Association between Traits and Haplotypes when Linkage Phase Is Ambiguous
Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M., & Poland, G. A. (2002) · The American Journal of Human Genetics · doi:10.1086/338688 · Cited by 1,777
A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other
Nyholt, D. R. (2004) · The American Journal of Human Genetics · doi:10.1086/383251 · Cited by 1,699

Show all 49 references

Sorted by citations

Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test
Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.029 · Cited by 2,373
Self-citation
Analysing biological pathways in genome-wide association studies
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2884 · Cited by 775
Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies
Wu, M. C., Kraft, P. L., Epstein, M. P., Taylor, D. M., Chanock, S. J., Hunter, D. J., & Lin, X. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.05.002 · Cited by 597
Self-citation
Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455
Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test
Zhao, N., Chen, J., Carroll, I. M., Ringel-Kulka, T., Epstein, M. P., Zhou, H., Zhou, J. J., Ringel, Y., Li, H., & Wu, M. C. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.04.003 · Cited by 306
Self-citation
Genetic mapping of quantitative trait loci in crops
XU, Y. C., Li, P., Yang, Z., & Xu, C. (2016) · The Crop Journal · doi:10.1016/j.cj.2016.06.003 · Cited by 303
Pathway analysis by adaptive combination of P‐values
Yu, K. F., Li, Q., Bergen, A. W., Pfeiffer, R. M., Rosenberg, P. S., Caporaso, N. E., Kraft, P. L., & Chatterjee, N. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20422 · Cited by 268
Comparison of statistical tests for disease association with rare variants
Basu, S., & Pan, W. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20609 · Cited by 215
Sequence Kernel Association Test for Quantitative Traits in Family Samples
Chen, H., Meigs, J. B., & Dupuis, J. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21703 · Cited by 212
Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach
Vounou, M., Nichols, T. E., & Montana, G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.002 · Cited by 209
Kernel-based whole-genome prediction of complex traits: a review
Morota, G., & Gianola, D. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00363 · Cited by 162
Gene set analysis of SNP data: benefits, challenges, and future directions
Fridley, B. L., & Biernacka, J. M. (2011) · European Journal of Human Genetics · doi:10.1038/ejhg.2011.57 · Cited by 146
HPA axis genetic variation, cortisol and psychosis in major depression
Schatzberg, A. F., Keller, J., Tennakoon, L., Lembke, A., Williams, G., Kraemer, F. B., Sarginson, J. E., Lazzeroni, L. C., & Murphy, G. M. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.129 · Cited by 131
Platelet CD36 surface expression levels affect functional responses to oxidized LDL and are associated with inheritance of specific genetic polymorphisms
Ghosh, A., Murugesan, G., Chen, K., Zhang, L., Wang, Q. K., Febbraio, M., Anselmo, R. M., Marchant, K., Barnard, J., & Silverstein, R. L. (2011) · Blood · doi:10.1182/blood-2011-02-338582 · Cited by 130
Unsupervised multiple kernel learning for heterogeneous data integration
Mariette, J., & Vialaneix, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx682 · Cited by 120
SNP Set Association Analysis for Familial Data
Schifano, E. D., Epstein, M. P., Bielak, L. F., Jhun, M. A., Kardia, S. L. R., Peyser, P. A., & Lin, X. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21676 · Cited by 110
Self-citation
Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges
Zeng, P., Shao, Z., & Zhou, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.042 · Cited by 97
Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and Statistical Foundations
Schaid, D. J. (2010) · Human Heredity · doi:10.1159/000312641 · Cited by 95
Multiple Genetic Variant Association Testing by Collapsing and Kernel Methods With Pedigree or Population Structured Data
Schaid, D. J., McDonnell, S. K., Sinnwell, J. P., & Thibodeau, S. N. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21727 · Cited by 93
Joint analysis of SNP and gene expression data in genetic association studies of complex diseases
Huang, Y.-T., VanderWeele, T. J., & Lin, X. (2014) · The Annals of Applied Statistics · doi:10.1214/13-aoas690 · Cited by 88
Self-citation
Multivariate Phenotype Association Analysis by Marker‐Set Kernel Machine Regression
Maity, A., Sullivan, P. F., & Tzeng, J. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21663 · Cited by 87
Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures
Ge, T., Feng, J., Hibar, D. P., Thompson, P. M., & Nichols, T. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.012 · Cited by 88
DASH: A Method for Identical-by-Descent Haplotype Mapping Uncovers Association with Recent Variation
Gusev, A., Kenny, E. E., Lowe, J. K., Salit, J., Saxena, R., Kathiresan, S., Altshuler, D., Friedman, J. M., Breslow, J. L., & Pe’er, I. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.04.023 · Cited by 88
Gene-Based Testing of Interactions in Association Studies of Quantitative Traits
Ma, L., Clark, A. G., & Keinan, A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003321 · Cited by 86
JOINT ANALYSIS OF SNP AND GENE EXPRESSION DATA IN GENETIC ASSOCIATION STUDIES OF COMPLEX DISEASES1Preprint
Huang, Y., Tyler, J., V, & Lin, X. (2013) · Cited by 88
Self-citation
Variation in folate pathway genes contributes to risk of congenital heart defects among individuals with Down syndrome
Locke, A. E., Dooley, K. J., Tinker, S. W., Cheong, S. Y., Feingold, E., Allen, E. G., Freeman, S. B., Torfs, C. P., Cua, C. L., Epstein, M. P., et al. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20518 · Cited by 84
Self-citation
Association of Type 2 Diabetes Susceptibility Variants With Advanced Prostate Cancer Risk in the Breast and Prostate Cancer Cohort Consortium
Machiela, M. J., Lindström, S., Allen, N. E., Haiman, C. A., Albanes, D., Barricarte, A., Berndt, S. I., Bueno-de-Mesquita, H. B., Chanock, S. J., Gaziano, J. M., et al. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws191 · Cited by 81
Association tests using kernel‐based measures of multi‐locus genotype similarity between individuals
Mukhopadhyay, I., Feingold, E., Weeks, D. E., & Thalamuthu, A. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20451 · Cited by 78
Functional Linear Models for Association Analysis of Quantitative Traits
Fan, R., Wang, Y., Mills, J. L., Wilson, A. F., Wilson, J., & Xiong, M. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21757 · Cited by 76
Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression
Tzeng, J., Zhang, D., Pongpanich, M., Smith, C., McCarthy, M. I., Sale, M. M., Worrall, B. B., Hsu, F., Thomas, D. C., & Sullivan, P. F. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.007 · Cited by 82
A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants
Broadaway, K. A., Cutler, D. J., Duncan, R., Moore, J. L., Ware, E. B., Jhun, M. A., Bielak, L. F., Zhao, W., Smith, J. A., Peyser, P. A., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.01.017 · Cited by 80
Self-citation
Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing
Pan, W. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20567 · Cited by 71
Kernel Machine SNP‐Set Testing Under Multiple Candidate Kernels
Wu, M. C., Maity, A., Lee, S., Simmons, E., Harmon, Q. E., Lin, X., Engel, S. M., Molldrem, J. J., & Armistead, P. M. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21715 · Cited by 72
Self-citation
Comparisons of multi‐marker association methods to detect association between a candidate region and disease
Ballard, D. H., Cho, J. H., & Zhao, H. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20448 · Cited by 74
Massively expedited genome-wide heritability analysis (MEGHA)
Ge, T., Nichols, T. E., Lee, P. H., Holmes, A. J., Roffman, J. L., Buckner, R. L., Sabuncu, M. R., & Smoller, J. W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1415603112 · Cited by 72
Single nucleotide polymorphism in the neuroplastin locus associates with cortical thickness and intellectual ability in adolescents
Desrivières, S., Lourdusamy, A., Tao, C., Toro, R., Jia, T., Loth, E., Medina, L. M., Kepa, A., Fernandes, A. R., Ruggeri, B., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.197 · Cited by 70
Combined linkage and association mapping reveals CYCD5;1 as a quantitative trait gene for endoreduplication in Arabidopsis
Sterken, R., Kiekens, R., Boruc, J., Zhang, F., Vercauteren, A., Vercauteren, I., Smet, L. D., Dhondt, S., Inzé, D., Veylder, L. D., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120811109 · Cited by 66
Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia
Tang, S., Buchman, A. S., Jager, P. L. D., Bennett, D. A., Epstein, M. P., & Yang, J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009482 · Cited by 61
Self-citation
Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies
Lin, X., Cai, T., Wu, M. C., Zhou, Q., Liu, G., Christiani, D. C., & Lin, X. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20610 · Cited by 62
Self-citation
Cigarette smoking increases copy number alterations in nonsmall-cell lung cancer
Huang, Y., Lin, X., Liu, Y., Chirieac, L. R., McGovern, R., Wain, J. R., Heist, R. S., Skaug, V., Zienolddiny, S., Haugen, A., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102769108 · Cited by 60
Self-citation
A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants
Pan, W., Kwak, I., & Wei, P. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.05.018 · Cited by 58
Predicting Genetic Values: A Kernel-Based Best Linear Unbiased Prediction With Genomic Data
Ober, U., Erbe, M., Long, N., Porcu, E., Schlather, M., & Simianer, H. (2011) · Genetics · doi:10.1534/genetics.111.128694 · Cited by 57
Robust linear regression methods in association studies
Lourenço, V., Pires, A. M., & Kirst, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr006 · Cited by 56
Gene-centric gene–gene interaction: a model-based kernel machine method
Li, S., & Cui, Y. (2012) · The Annals of Applied Statistics · doi:10.1214/12-aoas545 · Cited by 51
An Evolutionary Framework for Association Testing in Resequencing Studies
King, C. R., Rathouz, P. J., & Nicolae, D. L. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001202 · Cited by 54
Kernel methods for large-scale genomic data analysis
Wang, X., Xing, E. P., & Schaid, D. J. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu024 · Cited by 50
A Large-scale genetic association study of esophageal adenocarcinoma risk
Liu, C.-Y., Wu, M. C., Chen, F., Ter‐Minassian, M., Asomaning, K., Zhai, R., Wang, Z., Su, L., Heist, R. S., Kulke, M. H., et al. (2010) · Carcinogenesis · doi:10.1093/carcin/bgq092 · Cited by 50
Self-citation
Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies
Qu, L., Guennel, T., & Marshall, S. L. (2013) · Biometrics · doi:10.1111/biom.12095 · Cited by 47
Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies
Chen, J., Chen, W., Zhao, N., Wu, M. C., & Schaid, D. J. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21934 · Cited by 49
Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits
Zhan, X., Zhao, N., Plantinga, A., Thornton, T. A., Conneely, K. N., Epstein, M. P., & Wu, M. C. (2017) · Genetics · doi:10.1534/genetics.116.199646 · Cited by 47
Self-citation
A Guide to Illumina BeadChip Data Analysis
Wu, M. C., & Kuan, P. F. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7481-8_16 · Cited by 47
SBERIA: Set‐Based Gene‐Environment Interaction Test for Rare and Common Variants in Complex Diseases
Jiao, S., Hsu, L., Bezieau, S., Brenner, H., Chan, A. T., Chang-Claude, J. C., Marchand, L. L., Lemire, M., Newcomb, P. A., Slattery, M. L., et al. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21735 · Cited by 43
Genome-Wide Meta-Analysis of Myopia and Hyperopia Provides Evidence for Replication of 11 Loci
Simpson, C. L., Wojciechowski, R., Oexle, K., Murgia, F., Portas, L., Li, X., Verhoeven, V. J. M., Vitart, V., Schäche, M., Hosseini, S. M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107110 · Cited by 43
Evaluation of Clustering and Genotype Distribution for Replication in Genome Wide Association Studies: The Age-Related Eye Disease Study
Edwards, A. O., Fridley, B. L., James, K. M., Sharma, A. K., Cunningham, J. M., & Tosakulwong, N. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003813 · Cited by 43
iGWAS: Integrative Genome‐Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis
Huang, Y., Liang, L., Moffatt, M. F., Cookson, W., & Lin, X. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21905 · Cited by 39
Self-citation
Adjustment for Population Stratification via Principal Components in Association Analysis of Rare Variants
Zhang, Y., Guan, W., & Pan, W. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21691 · Cited by 38
Performance of a blockwise approach in variable selection using linkage disequilibrium information
Dehman, A., Ambroise, C., & Neuvial, P. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0556-6 · Cited by 36
A Brain‐wide association study of DISC1 genetic variants reveals a relationship with the structure and functional connectivity of the precuneus in schizophrenia
Gong, X., Lu, W., Kendrick, K. M., Pu, W., Wang, C., Li, J., Lu, G., Liu, Z., Liu, H., & Feng, J. (2014) · Human Brain Mapping · doi:10.1002/hbm.22560 · Cited by 36
U‐Statistics‐based Tests for Multiple Genes in Genetic Association Studies
Wei, Z., Li, M., Rebbeck, T. R., & Li, H. (2008) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2008.00473.x · Cited by 35
Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction
Zou, F., Huang, H., Lee, S., & Hoeschele, I. (2010) · Genetics · doi:10.1534/genetics.109.113688 · Cited by 35
Sequence Variants in the TLR4 and TLR6-1-10 Genes and Prostate Cancer Risk. Results Based on Pooled Analysis from Three Independent Studies
Lindström, S., Hunter, D. J., Grönberg, H., Stattin, P. E., Wiklund, F. E., Xu, J., Chanock, S. J., Hayes, R. B., & Kraft, P. (2010) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-09-0618 · Cited by 33
Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies
Hua, W., & Ghosh, D. (2015) · Biometrics · doi:10.1111/biom.12314 · Cited by 31
Self-citation
Region-Based Association Analysis of Human Quantitative Traits in Related Individuals
Belonogova, N. M., Svishcheva, G. R., van Duijn, C. M., Aulchenko, Y. S., & Axenovich, T. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065395 · Cited by 30
A review of kernel methods for genetic association studies
Larson, N. B., Chen, J., & Schaid, D. J. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22180 · Cited by 30
Generalized Functional Linear Models for Gene‐Based Case‐Control Association Studies
Fan, R., Wang, Y., Mills, J. L., Carter, T. C., Lobach, I. V., Wilson, A. F., Wilson, J., Weeks, D. E., & Xiong, M. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21840 · Cited by 30
A powerful test for multiple rare variants association studies that incorporates sequencing qualities
Daye, Z. J., Li, H., & Wei, Z. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks024 · Cited by 30
A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application
Ge, T., Nichols, T. E., Ghosh, D., Mormino, E. C., Smoller, J. W., & Sabuncu, M. R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.029 · Cited by 31
Self-citation
A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model
Yan, Q., Tiwari, H. K., Yi, N., Gao, G., Zhang, K., Lin, W., Lou, X., Cui, X., & Liu, N. (2015) · Human Heredity · doi:10.1159/000375409 · Cited by 28
FREGAT: an R package for region-based association analysis
Belonogova, N. M., Svishcheva, G. R., & Axenovich, T. I. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw160 · Cited by 28
Pathway-Based Analysis Using Genome-wide Association Data from a Korean Non-Small Cell Lung Cancer Study
Lee, D., Lee, G. K., Yoon, K., & Lee, J. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065396 · Cited by 26
Region-Based Association Test for Familial Data under Functional Linear Models
Svishcheva, G. R., Belonogova, N. M., & Axenovich, T. I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128999 · Cited by 25
A Kernel Regression Approach to Gene‐Gene Interaction Detection for Case‐Control Studies
Larson, N. B., & Schaid, D. J. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21749 · Cited by 25
FFBSKAT: Fast Family-Based Sequence Kernel Association Test
Svishcheva, G. R., Belonogova, N. M., & Axenovich, T. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099407 · Cited by 23
Risk Classification With an Adaptive Naive Bayes Kernel Machine Model
Minnier, J., Yuan, M., Liu, J. S., & Cai, T. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.908778 · Cited by 24
Global Analysis of Methylation Profiles From High Resolution CpG Data
Zhao, N., Bell, D. A., Maity, A., Staicu, A., Joubert, B. R., London, S. J., & Wu, M. C. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21874 · Cited by 23
A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia
Alam, M. A., Lin, H.-Y., Deng, H., Calhoun, V. D., & Wang, Y. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.08.027 · Cited by 23
ExPheWas: a platform for cis -Mendelian randomization and gene-based association scans
Legault, M., Perreault, L. L., Tardif, J., & Dubé, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac289 · Cited by 23
Identification of genetic association of multiple rare variants using collapsing methods
Sun, Y. V., Sung, Y. J., Tintle, N., & Ziegler, A. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20658 · Cited by 21
Imaging genetics — towards discovery neuroscience
Ge, T., Schumann, G., & Feng, J. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0023-1 · Cited by 21
A variance component based multi-marker association test using family and unrelated data
Wang, X., Morris, N. J., Zhu, X., & Elston, R. C. (2013) · BMC Genetics · doi:10.1186/1471-2156-14-17 · Cited by 20
Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test
Cai, T., Lin, X., & Carroll, R. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs015 · Cited by 20
Self-citation
Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure
Yoo, Y. J., Sun, L., Poirier, J. G., Paterson, A. D., & Bull, S. B. (2016) · Genetic Epidemiology · doi:10.1002/gepi.22024 · Cited by 19
Object-oriented regression for building predictive models with high dimensional omics data from translational studies
Zhao, L. P., & Bolouri, H. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.001 · Cited by 19
Identification of association between disease and multiple markers via sparse partial least-squares regression
Chun, H., Ballard, D. H., Cho, J. H., & Zhao, H. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20596 · Cited by 18
Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning
Aksman, L., Scelsi, M. A., Marquand, A. F., Alexander, D. C., Sebastien Ourselin, A., Altmann, A., & for, A. D. N. I. (2019) · Human Brain Mapping · doi:10.1002/hbm.24682 · Cited by 18
Associations of Progesterone Receptor Polymorphisms with Age at Menarche and Menstrual Cycle Length
Taylor, K. C., Small, C. M., Epstein, M. P., Sherman, S. L., Tang, W., Wilson, M. M., Bouzyk, M., & Marcus, M. (2010) · Hormone Research in Paediatrics · doi:10.1159/000316961 · Cited by 18
Self-citation
Integrative modeling of multi-platform genomic data under the framework of mediation analysis
Huang, Y. (2015) · Statistics in Medicine · doi:10.1002/sim.6326 · Cited by 17
Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method
Yan, Q., Weeks, D. E., Celedon, J. C., Tiwari, H. K., Li, B., Wang, X., Lin, W., Lou, X., Gao, G., Chen, W., et al. (2015) · Genetics · doi:10.1534/genetics.115.178590 · Cited by 17
Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics
Lu, H., Zhang, J., Jiang, Z., Zhang, M., Wang, T., Zhao, H., & Zeng, P. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.656545 · Cited by 20
Permutation-based variance component test in generalized linear mixed model with application to multilocus genetic association study
Zeng, P., Zhao, Y., Li, H., Ting, W., & Chen, F. (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0030-1 · Cited by 17
A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
Li, M., He, Z., Zhang, M., Zhan, X., Wei, C., Elston, R. C., & Lu, Q. L. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21790 · Cited by 16
Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits
Broadaway, K. A., Duncan, R., Conneely, K. N., Almli, L. M., Bradley, B., Ressler, K. J., & Epstein, M. P. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21901 · Cited by 15
Self-citation
Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection
Zhao, Y., Hongtu Zhu, B., Lu, Z., Knickmeyer, R. C., & Zou, F. (2019) · Genetics · doi:10.1534/genetics.119.301906 · Cited by 16
Regional replication of association with refractive error on 15q14 and 15q25 in the Age-Related Eye Disease Study cohort.
Simpson, C. L., Wojciechowski, R., Yee, S. S., Soni, P., Wilson, J., & Stambolian, D. (2013) · Europe PMC (PubMed Central) · Cited by 16
IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA
Shao, Z., Wang, T., Zhang, M., Jiang, Z., Huang, S., & Zeng, P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009250 · Cited by 13
A Fast Multiple‐Kernel Method With Applications to Detect Gene‐Environment Interaction
West, R. M., Lu, W., Holloway, S. T., Sale, M. M., Worrall, B. B., Williams, S. R., Hsu, F., & Tzeng, J. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21909 · Cited by 15
Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples
Yan, Q., Weeks, D. E., Tiwari, H. K., Yi, N., Zhang, K., Gao, G., Lin, W., Lou, X., Chen, W., & Liu, N. (2015) · Human Heredity · doi:10.1159/000445057 · Cited by 15
On Optimal Gene‐Based Analysis of Genome Scans
Bacanu, S. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21625 · Cited by 14
A unified framework for multi-locus association analysis of both common and rare variants
Shriner, D., & Vaughan, L. K. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-89 · Cited by 14
U-statistics in genetic association studies
Li, H. (2012) · Human Genetics · doi:10.1007/s00439-012-1178-y · Cited by 14

Authors: Kwee, L. C., Liu, D., Lin, X., Ghosh, D., & Epstein, M. P.
Affiliations: Emory University (US)
Brown University (US)
Harvard University (US)
Pennsylvania State University (US)
Corresponding author: Epstein, Michael P.
ORCID: Kwee, Lydia Coulter
Liu, Dawei
Lin, Xihong
Ghosh, Debashis
Epstein, Michael P.
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2008-02
Volume: 82
Issue: 2
Pages: 386-397
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2007.10.010
PMC: PMC2664991
OpenAlex: W2164143816
Cited by: 227
References: 49
Citations by year: 2026: 4 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 12 · 2018: 14 · 2017: 12 · 2016: 17 · 2015: 19 · 2014: 24 · 2013: 22 · 2012: 21
FWCI: 7.88
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1995 d)
Locations: The American Journal of Human Genetics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Collection of Biostatistics Research Archive (submittedVersion)
Funders: National Institutes of Health (5t32gm074909-07)
National Human Genome Research Institute (HG003618, R01HG003618)
National Cancer Institute (R01CA076404, R37CA076404, CA76404, R29CA076404)
National Institute of General Medical Sciences (T32-GM074909, GM074909)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords: quantitative trait mapping, tag SNPs, linkage disequilibrium, linear mixed models, semiparametric models, dimensionality reduction, statistical power, haplotypes, environmental variables
MeSH terms: Chromosome Mapping, Computer Simulation, Models, Genetic, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Quantitative Trait Loci

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Powerful and Flexible Multilocus Association Test for Quantitative Traits

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks