IVF Genetic Mutation Study

This project involved an inital summary of a small IVF data set using Python, SQL, and PostgreSQL. At first look, it looks like there could be some value in further pursing development of an at home test for predicting likelihood of success of IVF treatment, but more data is certainly required to be more conclusive. See the interactive Jupyter Notebook for full code and analsyis.

📓 View the Jupyter Notebook on GitHub

Summary

Fertility Status Correlation

• Variant Counts: Patients with primary infertility generally have a high number of genomic variants.
• Shared Loci Count: Primary infertility shows similarities in shared loci across patients.
• Mean AF & Pathogenic Variants: No significant difference in allele frequencies or pathogenic variants between fertile and infertile groups.

Genomic Insights

• Source of Variants: Germline and somatic classifications are consistent across patients, with no apparent impact on fertility status.
• Functional Impact: High-impact function counts are higher in patients with primary infertility. Damaging SIFT and PolyPhen counts also tend to be higher among these patients.

Reproductive-specific Variants

• IVF and Reproductive Variants: IVF and other reproductive gene variants don't significantly distinguish fertile from infertile, but IVF gene variant counts are lower in fertile individuals.
• Embryo & Hormone Genes: Minimal variations in these counts across the groups, suggesting no strong correlation with fertility status.

Other Observations

• Age-related and Recurrent Pregnancy Loss Variants: Age-related variants are slightly more frequent among fertile individuals. Recurrent pregnancy loss variants are common, but not exclusively in infertile patients.

Conclusion

Patients with primary infertility tend to have high variant counts and more high-impact functional variants. However, the specific role of these findings in fertility remains unclear without a richer data set.