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Omitting post-alignment processing and merging batch-based imputation: an efficient workflow for NIPT data imputation
Kaixin Wu1,2, Mei Zheng1,2, Peng He1,2
1Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, PR China.
Journal of Human Genetics
|August 11, 2026
Summary
This study developed an efficient imputation pipeline for non-invasive prenatal testing (NIPT) data. Simplifying workflows and improving data merging significantly reduces computational time and cost for maternal genetic trait screening.
Area of Science:
- Genomics
- Bioinformatics
- Maternal Health
Background:
- Non-invasive prenatal testing (NIPT) generates substantial low-depth sequencing data valuable for maternal genetic trait analysis.
- Standard genotype imputation workflows for NIPT data involve time-intensive post-alignment processing (GATK) with uncertain benefits for low-depth data.
- Merging imputation results from large cohorts, especially imputation information (INFO) scores, poses a significant challenge.
Purpose of the Study:
- To develop an efficient imputation pipeline optimized for low-depth NIPT data.
- To evaluate the necessity of standard GATK post-alignment steps for NIPT data.
- To validate a robust batch-merging strategy for imputation results, focusing on INFO scores.
Main Methods:
- Simulated low-depth NIPT data were used to assess imputation accuracy after omitting GATK post-alignment steps (e.g., duplicate marking, base quality score recalibration).
- A sample-size weighted averaging method was developed and validated for merging imputation INFO scores from batch-processed data.
- The optimized pipeline was applied to 517 real-world NIPT samples for maternal folate metabolism genotyping (MTHFR, MTRR loci).
Main Results:
- Omitting GATK post-alignment steps did not reduce imputation accuracy for simulated low-depth data but significantly decreased computational time.
- The sample-size weighted averaging method accurately merged INFO scores, producing results comparable to single-cohort imputation for high-quality variants.
- High genotype and allele concordance (>96% at GP80) was observed for MTHFR rs1801131 and MTRR rs1801394 when comparing the optimized pipeline to a sequencing capture method.
Conclusions:
- A simplified, computationally efficient imputation workflow for low-depth NIPT data has been validated.
- This optimized pipeline enables accurate maternal folate metabolism genotype assessment from existing clinical sequencing data.
- It offers a cost-effective strategy for large-scale genetic screening of maternal traits without additional experimental burden.
