Related Experiment Video
Updated: May 11, 2026
09:37
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
Published on: August 15, 2019
Evaluating power and type 1 error in large pedigree analyses of binary traits
Anna C Cummings1, Eric Torstenson, Mary F Davis
1Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
Plos One
|May 10, 2013
Summary
Simulating SNP data in complex pedigrees using GenomeSIMLA is effective. While dividing pedigrees for linkage analysis maintains low type 1 error, it reduces the power to detect genetic susceptibility loci.
Area of Science:
- Genetics
- Statistical genetics
- Computational biology
Background:
- Population isolates with large pedigrees offer advantages for genetic studies.
- Statistical analysis of complex pedigrees presents computational challenges.
- Relatedness correction is crucial for association tests, and pedigree simplification is needed for linkage analyses.
Purpose of the Study:
- To extend GenomeSIMLA for simulating SNP data in complex pedigrees.
- To evaluate statistical methods for analyzing genetic data in population isolates.
- To assess type 1 error rates and statistical power in simulated Amish pedigrees.
Main Methods:
- Extended GenomeSIMLA to simulate SNP data based on an Amish pedigree.
- Generated subpedigrees using PedCut for linkage analysis.
- Performed two-point and multipoint linkage using Merlin.
- Applied MQLS (Multipoint Quantitative Linkage Analysis Software) to whole and subpedigrees.
Main Results:
- No inflation of type 1 error was observed for MQLS on whole or subpedigrees.
- Low type 1 error rates were found for two-point and multipoint linkage analyses.
- MQLS power was reduced when applied to subpedigrees compared to the whole pedigree.
- Linkage analysis power was low for subpedigrees.
Conclusions:
- MQLS demonstrates appropriate type 1 error rates for complex Amish pedigree structures.
- Dividing pedigrees for linkage analysis does not inflate type 1 error.
- Pedigree division diminishes the power to detect linkage, impacting genetic susceptibility locus discovery.
Related Concept Videos
01:35Pedigree Analysis
Overview
01:35Pedigree Analysis
Overview
01:49Multiple Allele Traits
The Concept of Multiple Allelism
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:43Incomplete Dominance
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
01:14Errors In Hypothesis Tests
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.