UC Davis

Genome-to-phenome analyses in animal breeding often involves the estimation of genetic marker effects and breeding values, based on individual-level genotype and phenotype information. A genome-wide association study (GWAS) may also used to assess the correlations between single-nucleotide polymorphisms (SNPs) and the phenotype of interest. However, each animal breeder has a relatively small sample size, which could lead to an underpowered statistical analysis and lead to a higher chance of obtaining a false negative result. Using joint analyses by combining individual-level data before performing the analysis can increase statistical power and improve prediction accuracy, but animal breeders may be hesitant to share their animal's information with others, as this can reveal sequences responsible for their animals' economic value. One solution is to implement an encryption scheme to protect individual-level information. Homomorphic encryption for genotypes and phenotypes (HEGP) is a type of encryption that allows encrypted genomic data to be analyzed directly, providing a more secure method of estimating marker effects and breeding values when performing a joint analysis. In this study, HEGP is implemented on a real data set from a disease resilience study in pigs and evaluates the correlation between estimated marker effects and estimated breeding values of the encrypted and unencrypted data, respectively. The estimated percentages of genetic variance for each window obtained from a GWAS using the encrypted data were also compared to the results of the original study from which the data originated. Correlations between estimated marker effects, estimated breeding values, and estimated percentages of genetic variance of each window of the analyses using unencrypted data and encrypted data were all approximately 1, indicating that the implementation of HEGP in GWAS joint analyses produces effectively identical results and does not affect the precision of the obtained results.