Abstract The use of siblings to infer the factors influencing complex traits has been a cornerstone of quantitative genetics. Here we utilise siblings for a novel application: the identification of genetic architecture, specifically that in individuals with extreme trait values (e.g. in the top 1%). Establishing genetic architecture in these individuals is important because they are at greatest risk of disease and are most likely to harbour rare variants of large effect due to natural selection. We develop a theoretical framework that derives expected trait distributions of siblings based on an index sibling’s trait value and trait heritability. This framework is used to develop statistical tests that can infer complex genetic architecture in trait tails, distinguishing between polygenic, de novo and Mendelian tail architecture. We apply our tests to UK Biobank data here, while they can be used to infer genetic architecture in any cohort or health registry that includes siblings, without requiring genetic data. We describe how our approach has the potential to help disentangle the genetic and environmental causes of extreme trait values, to identify individuals likely to carry pathogenic variants for follow-up clinical genetic testing, and to improve the design and power of future sequencing studies to detect rare variants.

Sibling Similarity Can Reveal Key Insights Into Genetic Architecture