Abstract
Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 483-497 |
| Number of pages | 15 |
| Journal | Genetics |
| Volume | 204 |
| Issue number | 2 |
| DOIs | |
| State | Published - Oct 1 2016 |
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Keywords
- Constrained model
- Likelihood-ratio test
- Multivariate analysis
- Seemingly unrelated regression
- Sequential testing
ASJC Scopus subject areas
- Genetics
Cite this
Statistical methods for testing genetic pleiotropy. / Schaid, Daniel J; Tong, Xingwei; Larrabee, Beth; Kennedy, Richard B; Poland, Gregory A.; Sinnwell, Jason P.
In: Genetics, Vol. 204, No. 2, 01.10.2016, p. 483-497.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistical methods for testing genetic pleiotropy
AU - Schaid, Daniel J
AU - Tong, Xingwei
AU - Larrabee, Beth
AU - Kennedy, Richard B
AU - Poland, Gregory A.
AU - Sinnwell, Jason P.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
AB - Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
KW - Constrained model
KW - Likelihood-ratio test
KW - Multivariate analysis
KW - Seemingly unrelated regression
KW - Sequential testing
UR - http://www.scopus.com/inward/record.url?scp=84991508723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991508723&partnerID=8YFLogxK
U2 - 10.1534/genetics.116.189308
DO - 10.1534/genetics.116.189308
M3 - Article
C2 - 27527515
AN - SCOPUS:84991508723
VL - 204
SP - 483
EP - 497
JO - Genetics
JF - Genetics
SN - 0016-6731
IS - 2
ER -