Abstract: There is provided a computer-implemented method of analysing genetic data comprising: receiving a polygenic risk score for a target phenotype or target phenotype combination for a target individual; receiving individual genetic data for the target individual, the individual genetic data informative about an ancestry of the target individual; determining an individual position in an ancestry space using the individual genetic data; and calculating a genetic contribution to a risk for the target individual for the target phenotype or target phenotype combination using the polygenic risk score and the individual position. A corresponding apparatus is also provided.

Abstract: Disclosed is a method of analysing genetic data about an organism comprising receiving a plurality of input units. Each input unit comprises information about the association between genetic variants in a region of the genome and phenotypes or phenotype combinations. The method comprises carrying out iterations comprising, for each variant determining for which of the phenotypes or phenotype combinations the variant is causal based on the input units. If the variant is causal for phenotypes or phenotype combinations, a sampled effect size is determined of the variant on the phenotypes or phenotype combinations based on the input units and information about correlations between the variants in the region. For each variant, a prediction effect size is determined variant on the phenotypes or phenotype combinations based on an average across the iterations of the sampled effect sizes or of posterior effect sizes calculated using the sampled effect sizes.

Abstract: Disclosed is a method of analysing genetic data about an organism comprising receiving a plurality of input units. Each input unit comprises information about the association between genetic variants in a region of the genome and a target phenotype. One or more iterations are carried out comprising, for each variant, determining whether the variant is causal for the target phenotype. If the variant is causal, a sampled effect size is determined for each input unit based on the input units and correlations between the plurality of genetic variants in the region. The sampled effect size is non-zero for all of the input units. For each variant, a prediction effect size is determined for each input unit based on an average across the iterations of the sampled effect sizes for the input unit or of posterior effect sizes for the input unit calculated using the sampled effect sizes.

Abstract: The disclosure relates to analysing genetic data. In one arrangement, a method operates on input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants. A fine-mapping algorithm is applied to all or a subset of the input data to identify one or more independent phenotype-variant associations. A set of one or more fine-mapped variants is identified for each association. A fine-mapping predictive model is calculated on the basis of the input data and the set of fine-mapped variants. The effect on the target phenotype of the set of fine-mapped variants is subtracted from the input data to obtain residual association data. A machine learning algorithm is applied to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants.