Colloquium
The Department of Statistics
presents
Meg Gelder Ehm
US Bioinformatics
Glaxo Wellcome Inc.
Test Statistic to Detect
Errors in
Sibling-pair Relationships.
Abstract
Pharmaceuticals for complex diseases such as heart disease and diabetes
have proven difficult to develop. Identifying the genetic causes of these
diseases may illuminate pathways by which these diseases develop.
Improved statistical methods for analyzing genetic data are needed for
many aspects of this work. I propose a test statistic to detect errors in
sibling-pair relationships and investigate its properties.
Most currently available methods use likelihood based methods on multiplex
family data to identify typing or pedigree errors. These algorithms
cannot be applied in many sibling pair collections due to the lack of
parental information. Nonetheless, mis-specifying the relationships
between individuals has serious consequences for genetic studies: false
relationships bias the statistics designed to correlate genetic markers
with disease. To test the hypothesis that two individuals are indeed
siblings, I propose a test statistic based on the summation over a large
number of genetic markers of the number of alleles shared identical by
state (IBS) by a pair of individuals for each marker. The test statistic
has an approximate normal distribution under the null hypothesis and
extreme negative values correspond to non-sibling pairs. Power and
significance studies show the test statistic calculated using 50 unlinked
markers, has 96% power to detect half siblings and 100% power to detect
unrelated individuals as erroneous pairs with a 5% false positive rate.
Furthermore, extreme positive values of the test statistic correspond to
monozygotic twins.
Monday, February 23, 1998
4:10 P.M., 1070 CEB (Duncan Hall)
4:00 P.M.: Coffee, 1044 CEB
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