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Multiple Allele Traits01:49

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Dissection of heterogeneous phenotypes for quantitative trait mapping.

Heike Bickeböller1, Julia N Bailey, George J Papanicolaou

  • 1Department of Genetic Epidemiology, Georg-August University, Göttingen, Germany. hbickeb@gwdg.de

Genetic Epidemiology
|December 13, 2005
PubMed
Summary
This summary is machine-generated.

Genetic analysis of alcoholism and Kofendrerd personality disorder data revealed that phenotype selection and study population ethnicity significantly impact results. Empirical P-value determination is crucial for accurate genetic findings.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Analysis of Genetic Analysis Workshop 14 data from the Collaborative Study on the Genetics of Alcoholism (COGA) and simulated Kofendrerd personality disorder (KPD) data.
  • Both datasets exhibit genetic and phenotypic heterogeneity, including numerous related phenotypes beyond disease definitions.

Purpose of the Study:

  • To investigate methodological challenges encountered in genetic analyses of complex diseases.
  • To evaluate the impact of phenotype selection, covariates, and population ethnicity on genetic study outcomes.

Main Methods:

  • Genome-wide linkage and association analyses using microsatellites and single-nucleotide polymorphism (SNP) chip data.
  • Evaluation of phenotype selection (continuous vs. discrete) and inclusion of covariates.
  • Comparison of results from Affymetrix and Illumina SNP chip platforms.

Main Results:

  • Study outcomes were sensitive to phenotype selection, covariate inclusion, and population ethnicity.
  • Higher marker density in SNP chip data proved advantageous for association studies.
  • Different chromosomal segments may influence type I error rates, underscoring the need for empirical P-value determination.

Conclusions:

  • Methodological choices, particularly phenotype definition and population characteristics, significantly affect genetic analysis results.
  • Empirical P-value determination is essential for establishing statistical significance in complex disease genetics.
  • SNP chip marker density is a key consideration for association study power.