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- Publisher Website: 10.1007/s10519-021-10042-2
- Scopus: eid_2-s2.0-85101559070
- PMID: 33630212
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Article: On the Transformation of Genetic Effect Size from Logit to Liability Scale
Title | On the Transformation of Genetic Effect Size from Logit to Liability Scale |
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Authors | |
Keywords | Binary traits Effect size transformation Liability scale Liability threshold model Logit scale |
Issue Date | 25-Feb-2021 |
Publisher | Springer |
Citation | Behavior Genetics, 2021, v. 51, n. 3, p. 215-222 How to Cite? |
Abstract | Genetic effects on the liability scale are informative for describing the genetic architecture of binary traits, typically diseases. However, most genetic association analyses on binary traits are performed by logistic regression, and there is no straightforward method that transforms both effect size estimate and standard error from the logit scale to the liability scale. Here, we derive a simple linear transformation of the log odds ratio and its standard error for a single nucleotide polymorphism (SNP) to an effect size and standard error on the liability scale. We show by analytic calculations and simulations that this approximation is accurate when the disease is common and the SNP effect is small. We also apply this method to estimate the contribution of a SNP near the RET gene to the variance of Hirschsprung disease liability, and the age-specific contributions of APOE4 on the variance of Alzheimer’s disease liability. We discuss the approximate linear inter-relationships between genotype and effect sizes on the observed binary, logit, and liability scales, and the potential applications of the linear approximation to statistical power calculation for binary traits. |
Persistent Identifier | http://hdl.handle.net/10722/345835 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 1.092 |
DC Field | Value | Language |
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dc.contributor.author | Wu, Tian | - |
dc.contributor.author | Sham, Pak Chung | - |
dc.date.accessioned | 2024-09-04T07:05:49Z | - |
dc.date.available | 2024-09-04T07:05:49Z | - |
dc.date.issued | 2021-02-25 | - |
dc.identifier.citation | Behavior Genetics, 2021, v. 51, n. 3, p. 215-222 | - |
dc.identifier.issn | 0001-8244 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345835 | - |
dc.description.abstract | Genetic effects on the liability scale are informative for describing the genetic architecture of binary traits, typically diseases. However, most genetic association analyses on binary traits are performed by logistic regression, and there is no straightforward method that transforms both effect size estimate and standard error from the logit scale to the liability scale. Here, we derive a simple linear transformation of the log odds ratio and its standard error for a single nucleotide polymorphism (SNP) to an effect size and standard error on the liability scale. We show by analytic calculations and simulations that this approximation is accurate when the disease is common and the SNP effect is small. We also apply this method to estimate the contribution of a SNP near the RET gene to the variance of Hirschsprung disease liability, and the age-specific contributions of APOE4 on the variance of Alzheimer’s disease liability. We discuss the approximate linear inter-relationships between genotype and effect sizes on the observed binary, logit, and liability scales, and the potential applications of the linear approximation to statistical power calculation for binary traits. | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Behavior Genetics | - |
dc.subject | Binary traits | - |
dc.subject | Effect size transformation | - |
dc.subject | Liability scale | - |
dc.subject | Liability threshold model | - |
dc.subject | Logit scale | - |
dc.title | On the Transformation of Genetic Effect Size from Logit to Liability Scale | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s10519-021-10042-2 | - |
dc.identifier.pmid | 33630212 | - |
dc.identifier.scopus | eid_2-s2.0-85101559070 | - |
dc.identifier.volume | 51 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 215 | - |
dc.identifier.epage | 222 | - |
dc.identifier.eissn | 1573-3297 | - |
dc.identifier.issnl | 0001-8244 | - |