Multivariate Linear Regression Modeling for Influencing Factors of Fetal Y-Chromosome Concentration in Pregnant Women

Siqi Guo

doi:10.54097/ahg41748

Authors

Siqi Guo

DOI:

https://doi.org/10.54097/ahg41748

Keywords:

Y-chromosome concentration, multivariate linear regression, noninvasive prenatal testing, generalized additive model, cubic spline, gestational age, body mass index

Abstract

Noninvasive prenatal testing (NIPT) screens for chromosomal abnormalities by analyzing cell free fetal DNA in maternal peripheral blood, and the accurate quantification of fetal Y-chromosome concentration in male pregnancies is a decisive indicator of test validity. However, individual variability among pregnant women is substantial, so gestational age, body mass index (BMI), maternal age, and several other factors jointly modulate Y-chromosome concentration, which makes a single threshold rule inadequate across heterogeneous populations. The present study addresses the joint analysis of factors influencing Y-chromosome concentration by constructing a modeling framework centered on multivariate linear regression. The framework unifies a basic linear structure, generalized additive smoothing terms, and cubic spline basis expansions within a single estimation pipeline, and incorporates Pearson correlation and Spearman rank correlation, the F test, and the Akaike information criterion for variable screening and significance testing. Evaluation is carried out on a clinical NIPT dataset of 1082 records covering 12 obstetric indicators. On a held out test partition the proposed method attains a coefficient of determination of 0.687, a mean absolute error of 0.018, and a root mean square error of 0.024, which corresponds to an improvement of approximately 11.4% in the coefficient of determination over a purely linear baseline. The estimated regression coefficients indicate that gestational age exerts a significant positive effect on Y-chromosome concentration, that BMI exerts a significant negative modulating effect, and that maternal age, height, weight, and overall GC content contribute smaller yet statistically significant marginal effects when combined in the joint model. The proposed framework provides a quantitative basis for personalized decisions about the optimal NIPT testing window and is therefore relevant for improving the accuracy and timeliness of prenatal screening.

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