A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation - PubMed (original) (raw)

Comparative Study

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

M R Nelson et al. Genome Res. 2001 Mar.

Abstract

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.

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Figures

Figure 1

Figure 1

The three steps that constitute the combinatorial partitioning method.

Figure 2

Figure 2

A depiction of the combinatorial partitioning method applied two variable loci at a time (m = 2) over a range of k.

Figure 3

Figure 3

Plot of the proportion of variability explained by the 7710 retained sets of genotypic partitions. The sets are sorted by the proportion of variability explained and connected by a colored line corresponding to the number of partitions in each set.

Figure 4

Figure 4

Plot of the proportion of variability explained by the same sets shown in Figure 3, after grouping by the pairs of variable loci included in each set and sorting groups by the proportion of variability explained by the best partition for each group.

Figure 5

Figure 5

Plot of the cross-validated proportion of variability explained by the 7710 retained sets of genotypic partitions. The sets are sorted by the proportion of variability explained and connected by a colored line corresponding to the number of partitions in each set (smooth, nondecreasing lines). The proportion of variability explained for each set before cross-validation is shown by the jagged lines of corresponding colors.

Figure 6

Figure 6

Plot of the proportion of cross-validated variability explained by the same sets shown in Figure 5, after grouping by the pairs of variable loci included in each set and sorting groups by the proportion of variability explained by the best partition for each group.

Figure 7

Figure 7

The three selected sets of genotypic partitions with the greatest proportion of cross-validated variability explained are represented by a 3 × 3 grid of nine two-locus genotypes with shading to represent the partition each genotype belongs to. Below each partition is a smoothed histogram showing the ln Trig distribution within each partition (indicated by shading) and the mean and sample size of each partition.

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