| Title: | Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank |
| Journal: | International Journal of Epidemiology |
| Published: | 13 Feb 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29447406/ |
| DOI: | https://doi.org/10.1093/ije/dyy015 |
| Citations: | 89 (20 in last 2 years) as of 8 Aug 2024 |
Publication 2003
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Abstract
Background: To date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individual's own genotype on their birthweight, their mother's genotype, or both.</p>
Methods: We demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and fetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study.</p>
Results: Unlike simple regression models, our approach is unbiased when there is both a maternal and a fetal effect. The method can be used when either the individual's own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, and that there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype and reasonable power to detect whether it is a fetal and/or a maternal effect. We also identify a subset of birthweight-associated single nucleotide polymorphisms (SNPs) that have opposing maternal and fetal effects in the UK Biobank.</p>
Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.</p>
11 Keywords
- Biological Specimen Banks
- Birth Weight
- Female
- Humans
- Latent Class Analysis
- Maternal Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
- Pregnancy
- Regression Analysis
- United Kingdom
4 Authors
- Nicole M Warrington
- Rachel M Freathy
- Michael C Neale
- David M Evans
1 Application
| Application ID | Title |
|---|---|
| 12703 | Investigating the Genetic Architecture Underlying the Developmental Origins of Health and Disease |
Enabling scientific discoveries that improve human health