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Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.

Yee J, Kwon MS, Jin S, Park T, Park M - Biomed Res Int (2015)

Bottom Line: Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits.Hence, the information gain can be obtained for any phenotype distribution.Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Biophysics, Eulji University, Daejeon, Republic of Korea.

ABSTRACT
A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

No MeSH data available.


Comparison of the QMDR, GMDR, and m-spacing methods. Association strengths obtained by GMDR versus m-spacing (a) and by QMDR versus m-spacing (b) are compared for a simulated dataset. All three methods were used to evaluate the main effect as well as 2nd and 3rd order interactions. The dataset was designed to have one 2nd order interaction causal pair.
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fig3: Comparison of the QMDR, GMDR, and m-spacing methods. Association strengths obtained by GMDR versus m-spacing (a) and by QMDR versus m-spacing (b) are compared for a simulated dataset. All three methods were used to evaluate the main effect as well as 2nd and 3rd order interactions. The dataset was designed to have one 2nd order interaction causal pair.

Mentions: To show the plausibility of the proposed m-spacing method, a representative result is shown in Figure 3, using a dataset whose quantitative trait was generated from a normal distribution with a single causal SNP pair simulated, as described in the next section. The sample size of the dataset was 400, with 20 SNPs. In panel (a), the association strengths, obtained by m-spacing and GMDR, are plotted as horizontal and vertical coordinates, respectively. Filled triangles represent the main effects, while open circles are for the 2nd order interactions. Both methods identify the same single SNP pair having a prominent interaction plotted in the upper right corner. One of the SNPs was found to produce the main effect, in contrast to others. Again, the result is agreed by both methods. P values obtained by permutation are given in the boxes for those selected points. Association strengths of the 3rd order interactions are plotted with a plus sign. Because no 3rd order interaction is simulated into the dataset, the combinations of SNPs made by adding a single SNP to the causal pair are expected to have high association values. Those points are clustered near the identified causal pair in the upper right corner. In panel (b) of Figure 3, the same comparison was made using the result from m-spacing and QMDR. Both comparisons show consistent results between the proposed m-spacing method and GMDR or QMDR. Note that IGS instead of IG was used. The distribution of the IG values from a dataset would shift to a higher direction, with increased order of interactions. Thus, the more conditions applied, the less entropy may be left to find. In other words, as the order of interaction increases, the conditional entropy H(P∣G) tends to decrease, while H(P) remains the same. Therefore IGS is vital if one needs to compare the association strengths between genotypes from different orders of interactions. Figure 3 shows that the simulated causal pair has the largest IGS value among all points, from different orders of interactions.


Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.

Yee J, Kwon MS, Jin S, Park T, Park M - Biomed Res Int (2015)

Comparison of the QMDR, GMDR, and m-spacing methods. Association strengths obtained by GMDR versus m-spacing (a) and by QMDR versus m-spacing (b) are compared for a simulated dataset. All three methods were used to evaluate the main effect as well as 2nd and 3rd order interactions. The dataset was designed to have one 2nd order interaction causal pair.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4538333&req=5

fig3: Comparison of the QMDR, GMDR, and m-spacing methods. Association strengths obtained by GMDR versus m-spacing (a) and by QMDR versus m-spacing (b) are compared for a simulated dataset. All three methods were used to evaluate the main effect as well as 2nd and 3rd order interactions. The dataset was designed to have one 2nd order interaction causal pair.
Mentions: To show the plausibility of the proposed m-spacing method, a representative result is shown in Figure 3, using a dataset whose quantitative trait was generated from a normal distribution with a single causal SNP pair simulated, as described in the next section. The sample size of the dataset was 400, with 20 SNPs. In panel (a), the association strengths, obtained by m-spacing and GMDR, are plotted as horizontal and vertical coordinates, respectively. Filled triangles represent the main effects, while open circles are for the 2nd order interactions. Both methods identify the same single SNP pair having a prominent interaction plotted in the upper right corner. One of the SNPs was found to produce the main effect, in contrast to others. Again, the result is agreed by both methods. P values obtained by permutation are given in the boxes for those selected points. Association strengths of the 3rd order interactions are plotted with a plus sign. Because no 3rd order interaction is simulated into the dataset, the combinations of SNPs made by adding a single SNP to the causal pair are expected to have high association values. Those points are clustered near the identified causal pair in the upper right corner. In panel (b) of Figure 3, the same comparison was made using the result from m-spacing and QMDR. Both comparisons show consistent results between the proposed m-spacing method and GMDR or QMDR. Note that IGS instead of IG was used. The distribution of the IG values from a dataset would shift to a higher direction, with increased order of interactions. Thus, the more conditions applied, the less entropy may be left to find. In other words, as the order of interaction increases, the conditional entropy H(P∣G) tends to decrease, while H(P) remains the same. Therefore IGS is vital if one needs to compare the association strengths between genotypes from different orders of interactions. Figure 3 shows that the simulated causal pair has the largest IGS value among all points, from different orders of interactions.

Bottom Line: Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits.Hence, the information gain can be obtained for any phenotype distribution.Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Biophysics, Eulji University, Daejeon, Republic of Korea.

ABSTRACT
A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

No MeSH data available.