A computer-implemented method of analysing genetic data about an organism to calculate a polygenic risk score for the organism, the method comprising: receiving input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants in a region of interest of the genome of the organism; applying a fine-mapping algorithm to all or a subset of the input data to identify one or more independent phenotype-variant associations within the region of interest, comprising identifying for each association a set of one or more fine-mapped variants from the plurality of genetic variants, and determining for each fine-mapped variant an estimated probability of being causal for the phenotype-variant association, characterised in that the sum of the probabilities for the fine-mapped variants within the set adds to one, and the method further comprises: calculating, on the basis of the input data and the set of fine-mapped variants, a fine-mapping predictive model quantifying an effect on the target phenotype of the set of fine-mapped variants; subtracting from the input data, using the fine-mapping predictive model, the effect on the target phenotype of the set of fine-mapped variants to obtain residual association data; applying a machine learning algorithm to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants; and calculating a polygenic risk score for an individual for the target phenotype by applying to genetic data from the individual the fine-mapping predictive model and the further predictive correlations identified by the machine learning algorithm. receiving input data comprising strengths of association between one or more phenotypes including a target phenotype and a plurality of genetic variants in a region of interest of the genome of the organism; applying a fine-mapping algorithm to all or a subset of the input data to identify one or more independent phenotype-variant associations within the region of interest, comprising identifying for each association a set of one or more fine-mapped variants from the plurality of genetic variants, and determining for each fine-mapped variant an estimated probability of being causal for the phenotype-variant association, characterised in that the sum of the probabilities for the fine-mapped variants within the set adds to one, and the method further comprises: calculating, on the basis of the input data and the set of fine-mapped variants, a fine-mapping predictive model quantifying an effect on the target phenotype of the set of fine-mapped variants; subtracting from the input data, using the fine-mapping predictive model, the effect on the target phenotype of the set of fine-mapped variants to obtain residual association data; applying a machine learning algorithm to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants; and calculating a polygenic risk score for an individual for the target phenotype by applying to genetic data from the individual the fine-mapping predictive model and the further predictive correlations identified by the machine learning algorithm. calculating, on the basis of the input data and the set of fine-mapped variants, a fine-mapping predictive model quantifying an effect on the target phenotype of the set of fine-mapped variants; subtracting from the input data, using the fine-mapping predictive model, the effect on the target phenotype of the set of fine-mapped variants to obtain residual association data; applying a machine learning algorithm to the residual association data to identify further predictive correlations between the target phenotype and the plurality of genetic variants; and calculating a polygenic risk score for an individual for the target phenotype by applying to genetic data from the individual the fine-mapping predictive model and the further predictive correlations identified by the machine learning algorithm.