Applying generative adversarial networks to generate artificial genotype data in livestock

Genomics studies in livestock remain limited due to financial, ethical, or privacy considerations that restrict collaboration and data accessibility. To address these challenges, generative artificial intelligence models, such as Generative Adversarial Network (GAN), are being applied to generate biologically plausible synthetic data without compromising these standards. In this study, we trained a Principal Component (PC) Wasserstein GAN (PC-WGAN) with a gradient penalty to synthesize Single Nucleotide Polymorphism (SNP) in the principal component space. We used a simulated genotype dataset from 4,800 individuals and 37,540 SNP from chromosome 1, and retained 796 PC, which explained 90% of the total variance. These PC scores were used to generate synthetic PC scores and inverse transformed to SNP data. The validity of synthetic SNP data was assessed using both quantitative and visual approaches. Quantitatively, the Pearson correlation between the linkage disequilibrium (LD) values and minor allele frequency (MAF) distributions of the real and synthetic SNP datasets was calculated for comparison. Visually, the PC plots, LD decay curves, and MAF histograms were inspected to compare the distributions and structural patterns of the generated and real data. At 150 training epochs, the model effectively captured the major features of the real population, producing synthetic genotypes that overlapped with real genotypes in PC space and preserved long-range LD patterns and MAF distributions. While synthetic genotypes cannot replace real genomic data, our findings demonstrate that PC-WGAN produces biologically plausible artificial genotypes, offering a promising approach for future work in data augmentation, model benchmarking, and privacy-preserving livestock genomic research.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Bioinformatics

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Caballero Vargas, Edgar Giesus

Contributors dc:contributor

• Bresolin, Tiago
• Wheeler, Matthew B
• Roca, Alfred L.

• Generative Artificial Intelligence Models
• Synthetic Genotypes
• Snp

Statement dc:rights

• Copyright 2025 Edgar Caballero Vargas

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130182

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130182