Iterative full-genome phasing and imputation using neural networks

Abstract: In this project, a model based on a convolutional neural network have been developed with the aim of imputing missing genotype data. This model was based on an already existing autoencoder that was modified into a U-Net structure. The network was trained and used iteratively with the intention that the result would improve in each iteration. In order to do this, the output of the model was used as the input in the next iteration. The data used in this project was diploid genotype data, which was phased into haploids and then run separately through the network. In each iteration, the new haploids were generated based on the output haploids. These were used as in input in the next iteration. The result showed that the accuracy of the imputation improved slightly in every iteration. However, it did not surpass the same model that was trained for one single iteration. Further work is needed to make the model more useful.