Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 150-172 |
| Seitenumfang | 23 |
| Fachzeitschrift | Human heredity |
| Jahrgang | 81 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 22 Dez. 2016 |
Abstract
Objective: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies. Methods: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used. Results: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously. Conclusion: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.
ASJC Scopus Sachgebiete
- Biochemie, Genetik und Molekularbiologie (insg.)
- Genetik
- Medizin (insg.)
- Genetik (klinisch)
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in: Human heredity, Jahrgang 81, Nr. 3, 22.12.2016, S. 150-172.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - A general framework for the evaluation of genetic association studies using multiple marginal models
AU - Kitsche, Andreas
AU - Ritz, Christian
AU - Hothorn, Ludwig A.
PY - 2016/12/22
Y1 - 2016/12/22
N2 - Objective: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies. Methods: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used. Results: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously. Conclusion: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.
AB - Objective: In this study, we present a simultaneous inference procedure as a unified analysis framework for genetic association studies. Methods: The method is based on the formulation of multiple marginal models that reflect different modes of inheritance. The basic advantage of this methodology is that no explicit formulation of the correlation between the test statistics is required. Moreover, the genotype scores are considered as a quantitative explanatory variable, i.e., regression models are used. Results: The proposed approach covers a wide variety of endpoints (binary, count, quantitative, and time-to-event data). In addition, multiple endpoints of different types can be assessed simultaneously. This allows the detection of pleiotropic effects while taking the mode of inheritance into account. Moreover, multiple loci can be assessed simultaneously. Conclusion: The flexibility of the proposed approach is demonstrated while analyzing a variety of data examples.
KW - Generalized linear models
KW - Genetic association
KW - Pleiotropy
KW - Simultaneous inference
UR - http://www.scopus.com/inward/record.url?scp=85008324879&partnerID=8YFLogxK
U2 - 10.1159/000448477
DO - 10.1159/000448477
M3 - Article
C2 - 28002824
AN - SCOPUS:85008324879
VL - 81
SP - 150
EP - 172
JO - Human heredity
JF - Human heredity
SN - 0001-5652
IS - 3
ER -