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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

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Heritability01:06

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
Polygenic Traits01:18

Polygenic Traits

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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Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
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Genetic variance components estimation for binary traits using multiple related individuals.

Charalampos Papachristou1, Carole Ober, Mark Abney

  • 1Department of Mathematics, Physics, and Statistics, University of the Sciences, Philadelphia, Pennsylvania 19104, USA. c.papach@usp.edu

Genetic Epidemiology
|April 6, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing complex traits in large families. The method effectively identifies genetic factors influencing diseases like Type 2 diabetes (T2D).

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Area of Science:

  • Genetics
  • Epidemiology
  • Statistical Modeling

Background:

  • Complex traits are influenced by genetic and nongenetic factors.
  • Large pedigrees offer advantages for estimating these influences in epidemiological studies.

Purpose of the Study:

  • To develop a likelihood approach using generalized linear mixed models for dichotomous traits.
  • To incorporate covariates and account for correlations within individuals due to shared genetic background or random effects.

Main Methods:

  • A hierarchical model assessing individual trait probability and conditional independence.
  • Utilizing an automated Monte Carlo Expectation Maximization algorithm for parameter estimation due to high-dimensional integration.
  • Applying the method to a Hutterite pedigree for Type 2 diabetes (T2D) analysis.

Main Results:

  • The simulation study demonstrated reliable parameter estimates with sample sizes around 500.
  • Analysis of the Hutterite data revealed a significant genetic component in Type 2 diabetes (T2D) risk.
  • The genetic influence on T2D risk was particularly evident in younger and leaner individuals.

Conclusions:

  • The proposed statistical model is effective for analyzing complex traits in large, correlated populations.
  • Genetic factors play a substantial role in Type 2 diabetes (T2D) susceptibility.
  • Further research into genetic predispositions for T2D, especially in specific demographics, is warranted.