Generalised Transmission Mean Test extends causal genotype–phenotype inference to nuclear families with multiple offspring

Writing in *PLOS Genetics*, Yushi Tang and John D. Storey present the generalised Transmission Mean Test (gTMT), an extension of their earlier causal inference framework designed for parent–child trios. The original Transmission Mean Test (TMT) estimated causal genotype–phenotype relationships in population-sampled trios with a single child per family; the gTMT generalises this to nuclear families containing multiple siblings, a common structure in large cohort and biobank datasets.

The methodological advance centres on detecting genetic loci with non-zero average causal effects (ACE) on child phenotypes while accounting for the fact that siblings share random family-specific environmental and genetic effects. By modelling within-family transmission rather than relying solely on population-level association, the framework is designed to be less susceptible to confounding by population stratification — a persistent challenge in standard genome-wide association studies.

The gTMT is positioned as a tool for researchers working with family-based cohorts who wish to move beyond association to causal inference, without requiring complete family genotyping or strong modelling assumptions about sibling correlation structures. Tang and Storey provide theoretical derivations and, based on the abstract, simulation evidence supporting the method's validity. The paper is published in full open access in *PLOS Genetics* (doi: 10.1371/journal.pgen.1012231).