Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

Area of Science:

• Agricultural genomics
• Bioinformatics
• Data security

Background:

• FAIR principles promote data sharing in agricultural genome-to-phenome studies.
• Privacy and intellectual property concerns hinder data sharing.
• Existing methods lack robust solutions for secure genomic data analysis.

Purpose of the Study:

• To extend homomorphic encryption for genotypes and phenotypes (HEGP) for broader genome-to-phenome applications.
• To enable secure collaborative genomic analyses while preserving data confidentiality.
• To facilitate the adoption of FAIR principles in agricultural research.

Main Methods:

• Extension of the homomorphic encryption for genotypes and phenotypes (HEGP) method.
• Application of HEGP to linear mixed models, including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP).
• Integration of HEGP with Bayesian variable selection methods for genetic analyses.

Main Results:

• HEGP is suitable for various genetic analyses, including parameter estimation, genomic prediction, and genome-wide association studies (GWAS).
• The extended HEGP method maintains data confidentiality during statistical analyses.
• Demonstrated applicability to complex genetic models commonly used in quantitative trait genetics.

Conclusions:

• The advanced HEGP method offers a secure and efficient approach for collaborative agricultural genomic research.
• HEGP effectively addresses privacy and intellectual property barriers in genome-to-phenome studies.
• This facilitates greater data sharing and reusability, aligning with FAIR principles.