ReviewHuman heredity2010
Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.
Review in Human heredity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
57 citing papers in PubMed.
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Article
- Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.Frontiers in genetics · 2022Article
- Unpaired data empowers association tests.Bioinformatics (Oxford, England) · 2021Article
- A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation.Frontiers in genetics · 2021Review
- Article
- Semblance: An empirical similarity kernel on probability spaces.Science advances · 2019Article
- Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype.BMC genetics · 2019Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples.Genetics · 2018Article
- KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.The annals of applied statistics · 2018Article
- Relating drug response to epigenetic and genetic markers using a region-based kernel score test.BMC proceedings · 2018Article
- Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.Scientific reports · 2017Article
- Article
- Article
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits.Current genomics · 2016Article
- Filtering genetic variants and placing informative priors based on putative biological function.BMC genetics · 2016Article
- Comparing strategies for combined testing of rare and common variants in whole sequence and genome-wide genotype data.BMC proceedings · 2016Article
- Testing for polygenic effects in genome-wide association studies.Genetic epidemiology · 2015Article
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Abstract
Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1].
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