PRS model for hypertensive disorders of pregnancy

Jung, S. H., Kim, H., Jung, Y. M., Shivakumar, M., …, Kim, D. & Lee, S. M. (2024) Cardiovascular risk according to healthy lifestyle with genetic predisposition of hypertensive disorders of pregnancy.

---

Hypertensive disorders of pregnancy (O15_HYPTENSPREG)

FinnGen_r8_PRScs_auto.gz

Data Source (GWAS summary statistics)

• FinnGen Release 8 (R8): Includes association data at 20,175,454 variants for 2,202 endpoints based on 342,499 Finnish individuals (190,879 females and 151,620 males).
• HDP Cases: Identified using ICD codes:
  • ICD-8: 63701, 63703, 63704, 63709, 63710, 63799, 66120
  • ICD-9: 642
  • ICD-10: O10, O11, O13, O14, O15, O16
  • Result: 13,071 cases and 177,808 controls.
• GWAS Analysis: Performed by FinnGen using SAIGE, adjusting for:
  • Sex
  • Age
  • 10 principal components (PCs)
  • Genotyping batch

PRS Generation

1. Data Preparation

• FinnGen summary statistics were mapped from GRCh38 to GRCh37/hg19 using the liftOver tool to ensure compatibility with UK Biobank genotyped data (GRCh37/hg19).

2. PRS Construction

• Bayesian Prediction Method:
  We used PRS-CS to infer the posterior mean effect size of variants, leveraging the 1000 Genomes Project (phase 3 EUR) as the external linkage disequilibrium (LD) reference panel.
  • A total of 1,102,676 mapped variants were used.
• Computation:
  Individual PRSs were calculated as a weighted sum of risk alleles using beta coefficients from PRS-CS. This was implemented using PLINK v1.90 with the –score command.

References

1. Kurki, Mitja I., et al. "FinnGen provides genetic insights from a well-phenotyped isolated population." Nature 613.7944 (2023): 508-518.
2. Zhou, Wei, et al. "Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies." Nature genetics 50.9 (2018): 1335-1341.
3. Nassar, Luis R., et al. "The UCSC genome browser database: 2023 update." Nucleic acids research 51.D1 (2023): D1188-D1195.
4. Ge, Tian, et al. "Polygenic prediction via Bayesian regression and continuous shrinkage priors." Nature communications 10.1 (2019): 1776.
5. Chang, Christopher C., et al. "Second-generation PLINK: rising to the challenge of larger and richer datasets." Gigascience 4.1 (2015): s13742-015.