A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.

By Matthew R. Nelson, Sharon L. R. Kardia, R. Ferrell, Charles F. Sing. Genome Research, 2001

392 citations recorded by the source corpus.

Recent advances in genome research have accelerated the process of locating candidate genes and the variable sites within them and have simplified the task of genotype measurement. The development of statistical and computational strategies to utilize information on hundreds -- soon thousands -- of variable loci to investigate the relationships between genome variation and phenotypic variation has not kept pace, particularly for quantitative traits that do not follow simple Mendelian patterns of inheritance. We present here the combinatorial partitioning method (CPM) that examines multiple genes, each containing multiple variable loci, to identify partitions of multilocus genotypes that predict interindividual variation in quantitative trait levels. We illustrate this method with an application to plasma triglyceride levels collected on 188 males, ages 20--60 yr, ascertained without regard to health status, from Rochester, Minnesota. Genotype information included measurements at 18 diallelic loci in six coronary heart disease--candidate susceptibility gene regions: APOA1--C3--A4, APOB, APOE, LDLR, LPL, and PON1. To illustrate the CPM, we evaluated all possible partitions of two-locus genotypes into two to nine partitions (approximately 10(6) evaluations). We found that many combinations of loci are involved in sets of genotypic partitions that predict triglyceride variability and that the most predictive sets show nonadditivity. These results suggest that traditional methods of building multilocus models that rely on statistically significant marginal, single-locus effects, may fail to identify combinations of loci that best predict trait variability. The CPM offers a strategy for exploring the high-dimensional genotype state space so as to predict the quantitative trait variation in the population at large that does not require the conditioning of the analysis on a prespecified genetic model.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3823ff645a0b897a349a3ddc8529dd321c956195

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

A random forest approach to the detection of epistatic interactions in case-control studies (2009, methodology): “Nelson et al put forward a combinatorial partitioning method (CPM) that exhaustively searched for the combinatorial genotype group that has the most significant difference in the mean of the responding continuous phenotype [13].”
Trimming, weighting, and grouping SNPs in human case-control association studies. (2001, methodology): “Focusing on partitions comprising two marker loci each, Nelson et al. (2001) showed that this approach identified biological interactions between loci.”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “For higher-order interactions, exhaustive ﬁ lter methods have been developed (e.g., Multifactor Dimensionality Reduction (MDR) (Ritchie et al., 2001) or the Combinatorial Partitioning Method (CPM) (Nelson et al., 2001)) and shown to be able to detect SNP-SNP interactions across ≥ 3 orders.”
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases (2003, methodology): “In addition to the physiological epistasis methods of Cheverud and Routman [1995], exploratory data analysis methods such as the combinatorial partitioning method (CPM) have been developed for generating hypotheses about epistatic effects on quantitative trait variation [Nelson et al., 2001].”
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix (2005, methodology): “As they are caused by interactions among genes, an increasing number of studies (Hoh et al, 2001; Nelson et al, 2001; Ritchie et al, 2001; Cordell and Clayton, 2002) have employed multilocus methods to detect the causative genes.”
Mapping As You Go (2004, background): “6c, the genotype(2001, 2002) for further descriptions of the influences combination AABBCC has the highest performanceof context dependencies in mapping populations and (line plot) but genotype aa has the highest value whensome plant breeding and evolutionary consequences. averaged across all backgr”
Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. (2001, methodology): “The development of MDR was motivated by the limitations of the generalized linear model for detection and characterization of gene-gene (Templeton 2000) and gene-environment (Schlichting and Pigliucci 1998) interactions and by the success of data-reduction methods for quantitative traits (Nelson et ”
Bayesian inference of epistatic interactions in case-control studies (2007, methodology): “Several approaches have been developed to detect epistasis, including the combinatorial partitioning method (CPM”
Detecting gene-gene interactions that underlie human diseases (2009, methodology): “Recursive partitioning approaches ( Box 2 ) have been used as an alternative to traditional regression methods for detecting the genetic loci and their interactions that influence a phenotypic outcom”