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A statistical model for the identification of genes governing the incidence of cancer with age.

Das K, Wu R - Theor Biol Med Model (2008)

Bottom Line: This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects.The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression.Computer simulation was used to examine the statistical behavior of the model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of Florida, Gainesville, FL 32611, USA. Kiranmoy@stat.ufl.edu

ABSTRACT
The cancer incidence increases with age. This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects. Also, the incidence of cancer with ages can be controlled by genes. Here we present a dynamic statistical model for explaining the epidemiological pattern of cancer incidence based on individual genes that regulate cancer formation and progression. We incorporate the mathematical equations of age-specific cancer incidence into a framework for functional mapping aimed at identifying quantitative trait loci (QTLs) for dynamic changes of a complex trait. The mathematical parameters that specify differences in the curve of cancer incidence among QTL genotypes are estimated within the context of maximum likelihood. The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression. Computer simulation was used to examine the statistical behavior of the model. The model can be used as a tool for explaining the epidemiological pattern of cancer incidence.

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Related in: MedlinePlus

Curves for the number of cancer clones changing with age, determined by three different QTL genotypes AA, Aa, and aa, using given parameter values (solid) and estimated values (broken) with different heritabilities (H2) for a sample size of n = 400. (A) Group 1, H2 = 0.1, (B) Group 1, H2 = 0.4, (C) Group 2, H2 = 0.1, and (D) Group 2, H2 = 0.4.
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Figure 2: Curves for the number of cancer clones changing with age, determined by three different QTL genotypes AA, Aa, and aa, using given parameter values (solid) and estimated values (broken) with different heritabilities (H2) for a sample size of n = 400. (A) Group 1, H2 = 0.1, (B) Group 1, H2 = 0.4, (C) Group 2, H2 = 0.1, and (D) Group 2, H2 = 0.4.

Mentions: Figures 1 and 2 illustrate the shapes of estimated age-dependent cancer incidence curves for each QTL genotype, comparing with those of given curves. In general, the estimated curves are consistent with those given curves even when the heritability (0.1) and sample size (200) are modest, suggesting that the model can reasonably detect the genetic control of cancer incidence curves. In practice, our model can formulate a number of meaningful hypotheses, e.g., (7)-(9). In this study, these hypothesis tests were not performed because no real data are presently available.


A statistical model for the identification of genes governing the incidence of cancer with age.

Das K, Wu R - Theor Biol Med Model (2008)

Curves for the number of cancer clones changing with age, determined by three different QTL genotypes AA, Aa, and aa, using given parameter values (solid) and estimated values (broken) with different heritabilities (H2) for a sample size of n = 400. (A) Group 1, H2 = 0.1, (B) Group 1, H2 = 0.4, (C) Group 2, H2 = 0.1, and (D) Group 2, H2 = 0.4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2365934&req=5

Figure 2: Curves for the number of cancer clones changing with age, determined by three different QTL genotypes AA, Aa, and aa, using given parameter values (solid) and estimated values (broken) with different heritabilities (H2) for a sample size of n = 400. (A) Group 1, H2 = 0.1, (B) Group 1, H2 = 0.4, (C) Group 2, H2 = 0.1, and (D) Group 2, H2 = 0.4.
Mentions: Figures 1 and 2 illustrate the shapes of estimated age-dependent cancer incidence curves for each QTL genotype, comparing with those of given curves. In general, the estimated curves are consistent with those given curves even when the heritability (0.1) and sample size (200) are modest, suggesting that the model can reasonably detect the genetic control of cancer incidence curves. In practice, our model can formulate a number of meaningful hypotheses, e.g., (7)-(9). In this study, these hypothesis tests were not performed because no real data are presently available.

Bottom Line: This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects.The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression.Computer simulation was used to examine the statistical behavior of the model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of Florida, Gainesville, FL 32611, USA. Kiranmoy@stat.ufl.edu

ABSTRACT
The cancer incidence increases with age. This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects. Also, the incidence of cancer with ages can be controlled by genes. Here we present a dynamic statistical model for explaining the epidemiological pattern of cancer incidence based on individual genes that regulate cancer formation and progression. We incorporate the mathematical equations of age-specific cancer incidence into a framework for functional mapping aimed at identifying quantitative trait loci (QTLs) for dynamic changes of a complex trait. The mathematical parameters that specify differences in the curve of cancer incidence among QTL genotypes are estimated within the context of maximum likelihood. The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression. Computer simulation was used to examine the statistical behavior of the model. The model can be used as a tool for explaining the epidemiological pattern of cancer incidence.

Show MeSH
Related in: MedlinePlus