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The accurate measure of fetal fraction is important to ensure the results of noninvasive prenatal testing. However, measuring fetal fraction could require a substantial amount of data and additional costs. Therefore, this study proposes an alternative method of measuring fetal fraction with a limited sample size and low sequencing reads. Adaptive machine-learning algorithms customized to each laboratory's environment, were used to measure fetal fraction. Pregnant women with female fetuses were tested to exclude the bias caused by training data of women carrying male fetuses. The accuracy of fetal DNA fraction prediction was enhanced by increasing the training sample size. When trained with 1,000 samples (males) and tested with 45 samples (females), the optimal bin sizes using the read count and size features were 300 kb and 800 kb, respectively. Comparing the new 300-kb bin to the 50-kb bin used by SeqFF with 4,000-5,000 training samples, the correlation was approximately 3-5% higher with the 300-kb bin. Therefore, we propose an effective and tailored method to measure fetal fraction in individual laboratories with limited sample collecting conditions and relatively low-coverage sequencing data.

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GenomomFF: Cost-Effective Method to Measure Fetal Fraction by Adaptive Multiple Regression Techniques With Optimally Selected Autosomal Chromosome Regions