Analysis of quantitative genetic traits in a huge data set

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The genetic basis of quantitative trait variation and covariation is central to human genetics, evolutionary biology, and plant and animal breeding. In medical genetics many diseases, including schizophrenia, heart disease and cancer, are complex traits with continuous phenotypes and liabilities, which have multiple genome variants contributing genetic variance. In evolutionary biology fitness is largely due to such quantitative traits (e.g., fecundity, longevity). In plant and animal breeding most of the economically important traits are quantitative traits (e.g., milk, meat, and grain yields, environmental footprint, fecundity). Huge datasets are needed for statistical genomics because many variants (probably thousands), which can be clustered together, contribute to any individual quantitative trait and their effects can combine in complex ways (additive, dominant, epistatic). Moreover, important portions of the genetic variance of quantitative traits are controlled by variants that are rare, have small effect sizes or are highly correlated with other variants. The effects of such quantitative trait variants can only be separated when very powerful statistical models are used in very large data sets.We will analyse the genetic basis of 25 quantitative traits at the molecular level by creating and analysing a dataset containing genome sequences, pedigrees and trait records of 325000 pigs from the world's biggest commercial breeding programme. The dataset will be created and analysed using imputation and analysis algorithms based on those that we developed to support the breeding programme.The size of the dataset and the quality of the data will allow us to address three big questions:-1. Which genome variants control which quantitative traits, how do they control them and how do the multiple variants that control a single trait interact?2. What kinds of mechanisms cause traits to co-vary? To what extent does pleiotropy and linkage disequilibrium contribute? What is the distribution of the magnitude and sign of joint effects of genomic regions on pairs of traits? 3. To what extent do huge data sets help us address these questions? For the first time we have the technology to generate genome sequence data for hundreds of thousands of individuals at low cost and the computer power to store and analyse such data.The aim of this project is to harvest scientific benefits from a 15 year billion dollar pig breeding program. Our previous projects asked how statistical genomics helps animal breeding; this project asks how animal breeding helps statistical genomics.