| Title: | A generalized test of genotype-phenotype causality in population-sampled nuclear families. |
| Journal: | PLOS Genetics |
| Published: | 15 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42455868/ |
| DOI: | https://doi.org/10.1371/journal.pgen.1012231 |
| URL: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1012231&type=printable |
Publication 20273
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Abstract
We recently developed a causal inference framework and test-the Transmission Mean Test (TMT)-to identify causal genotype-phenotype relationships in population-sampled parent-child trios, where one child per family is observed. Here, we establish the generalized TMT (gTMT) for population-sampled nuclear families, allowing multiple offspring per family. This extension focuses on detecting genetic loci with non-zero average causal effects (ACE) on child phenotypes, taking into account that siblings share similar random family-specific effects. We construct a potential outcomes trait model that considers both individual-level and family-level heterogeneity, captures additive and non-additive genetic effects, and accommodates both quantitative (continuous or count) and dichotomous traits. We design an unbiased estimate dgTMT of the ACE and develop a sampling variance estimate σ^gTMT2 to form a statistic testing the null hypothesis of no causal effect. We provide both theory and empirical evidence demonstrating that gTMT is robust to confounding factors such as the population structure and family-specific effects. We analyze nuclear families in the UK Biobank as an illustrative example of the gTMT in action. When parental genotypes are missing, we propose to further extend gTMT by using Bayesian calculations on child genotypes to model parental genotypes as intermediate random variables.</p>
2 Authors
- Yushi Tang
- John D Storey
1 Application
| Application ID | Title |
|---|---|
| 104628 | Delineating the relationship between human evolutionary history on the architectural complexity and predictability of traits |
Enabling scientific discoveries that improve human health