Utilizing non-invasive prenatal test sequencing data for human genetic investigation

Area of Science:

• Genomics and Bioinformatics
• Human Genetics
• Reproductive Medicine

Background:

• Non-invasive prenatal testing (NIPT) uses cell-free fetal DNA in maternal plasma for trisomy detection.
• Widespread NIPT adoption generates a vast human genetic resource.
• Existing methods are limited for analyzing large-scale, low-depth NIPT data.

Purpose of the Study:

• To develop and validate methods for analyzing large-scale, low-depth NIPT data.
• To enable genetic variant detection, imputation, and association studies using NIPT data.
• To establish a foundation for NIPT data utilization in medical genetic research.

Main Methods:

• Developed customized algorithms and software for NIPT data analysis.
• Applied methods for genetic variant detection, genotype imputation, and family relatedness.
• Performed population structure inference and genome-wide association analysis on maternal genomes.

Main Results:

• Accurate allele frequency estimation and high genotype imputation accuracy (R² > 0.84) at low sequencing depths (0.1×–0.3×).
• Effective classification of duplicates and first-degree relatives, with robust principal-component analysis.
• Accurate estimation of genetic effect sizes (R² > 0.81) across platforms.

Conclusions:

• The developed methods provide a robust framework for analyzing large-scale NIPT data.
• These techniques facilitate the exploration of genetic variations and their phenotypic associations.
• NIPT data can be effectively leveraged for significant advancements in medical genetic research.