Next generation imputation for huge data sets

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Knowledge gained from genome sequencing has great potential for increasing the direction and rate of genetic change in livestock breeding, and biological discovery in animal science. However huge numbers of individuals will need to be sequenced to unlock this potential, and the current cost of sequencing for livestock is several hundreds or thousands of pounds per individual. This will remain a barrier for using this data routinely until the unit cost is of the order of tens of pounds. One promising approach to reducing costs whilst maintaining the quality of the resulting data is to use technology called next-generation sequencing with low coverage (lcNGS). With lcNGS, large numbers of individuals can have their sequences sampled at low cost per individual, but each individual sequence will have substantial missing information. Accuracy is restored by inferring missing data using a process known as imputation. In livestock this process is made more efficient by pedigree structures in livestock populations.Imputation using single nucleotide polymorphism (SNP) data from chips has been successfully applied in livestock. However, these methods are not optimal for the imputation from lcNGS data for several reasons. (i) SNP-chip genotypes are highly accurate and data points are missing only occasionally due to technical issues. In contrast, lcNGS data has much less certainty over the true genotype at a particular locus, and the missing data is randomly spread over the whole genome. (ii) SNP-chip genotypes cover only a small fraction of the genetic variation present in the genome in comparison to sequence data, so the computational techniques for imputing sequence data need to be much more efficient for practical use. (iii) The range of the data produced by lcNGS is rapidly evolving, requiring next-generation imputation algorithms to be very flexible. The imputation algorithm proposed will address these issues from a novel direction by combining two approaches: heuristic and probabilistic. Heuristic algorithms use basic principles of inheritance and so are fast, and accurate. They are well-suited to animal breeding since they use pedigree to make inferences from the abundance of closely-related individuals from large families, with large portions of the genome shared between pairs of individuals. However, heuristic methods can fail if such data is lacking or is unreliable across all or parts of the genome. Probabilistic algorithms primarily use Hidden Markov Models to mimic inheritance statistically and are computationally more demanding, slower, and inherently less accurate than heuristic algorithms. They have been developed primarily for application to human populations in which the pedigree structures, for example small sibships, are not well-suited to exploiting the power of heuristic algorithms. The proposed algorithm will obtain synergy from combining the two approaches as they have complementary strengths in the recovery of information and computational efficiency.The overall objective is therefore to develop a generic imputation system that is capable of imputing in data sets of the order of millions of animals, can cope with the wide variety of data types that may appear from lcNGS. New heuristic approaches will be adopted to develop data that can be integrated with probabilistic approaches and combined into a novel hybrid algorithm. Efficient data handling and storage frameworks, and a user interface will be developed to ensure the algorithm is computationally efficient, easy-to-use, and readily available to users. The algorithm will be benchmarked using a range of real and simulated data sets and historical, real SNP-chip data to ensure it remains backwards compatible to current or previous technology. The availability of the algorithm will enable breeders to accumulate sequence data on millions of animals at low unit cost, and in turn prompt greater accuracy of selection and innovation in breeding goals.