Capture-recapture for -omics data meta-analysis

Abstract

One of the major goals of modern -omics studies, in particular genome-wide association studies (GWASs), is to understand the polygenicity of various traits, i.e. the number of genetic factors causally determining them. Analogous measures could also be used to estimate the number of trait markers from non-genetic studies, such as proteomics or transcriptomics. Here, we describe how capture-recapture (C-R) models, originating in animal ecology, can be applied to this task. Our approach works by comparing the lists of trait-associated genes (or other markers) from several studies. In contrast to existing methods, C-R is specifically designed to make use of heterogeneous input studies, differing in analysis methods, populations or other factors: it extrapolates from their variability to estimate how many causal genes still remain undetected. We present a brief tutorial on C-R models, and demonstrate our proposed usage of it with code examples and simulations. We then apply it to GWASs and proteomic studies of preterm birth, a major clinical problem with largely unknown causes. The C-R estimates a relatively low number of causal genes for this trait, but many still undetected protein markers, suggesting that diverse environmentally-initiated pathways can lead to this clinical outcome.