Figure S1 showed optimally adaptive reference samples extracted from all reference samples. Figure S2 showed that GC correction played an important role in reducing the CV. Figures S3.1, S3.2, S4.1, S4.2, S5 and S6 represented similar results to our adaptive sample selection. Figure S7 represented the relationship of the reads fractions and the GC contents of samples. (DOCX 2063Â kb

Byung Kim (3407873)

Hailing Zheng (647377)

Hee Joo (3505313)

HeeJung Jung (3505310)

Hongliang Chen (538815)

Hwanjong Kwak (3505325)

Hyungsik Chu (3505316)

JeongSub Kwon (3505319)

Jong Bhak (64076)

Jungsu Ha (3505322)

Kyudong Han (35824)

Kyusang Lee (2573419)

Min Kim (218786)

Minae An (3505328)

SeungJae Lee (3505307)

Sung Han (3455408)

Sunghoon Park (408999)

Sunshin Kim (137908)

Xinqiang Zhu (509109)

BMC Medical Genomics

PubMed

Abstract Background Noninvasive prenatal testing (NIPT) using massively parallel sequencing of cell-free DNA (cfDNA) is increasingly being used to predict fetal chromosomal abnormalities. However, concerns over erroneous predictions which occur while performing NIPT still exist in pregnant women at high risk for fetal aneuploidy. We performed the largest-scale clinical NIPT study in Korea to date to assess the risk of false negatives and false positives using next-generation sequencing. Methods A total of 447 pregnant women at high risk for fetal aneuploidy were enrolled at 12 hospitals in Korea. They underwent definitive diagnoses by full karyotyping by blind analysis and received aneuploidy screening at 11–22 weeks of gestation. Three steps were employed for cfDNA analyses. First, cfDNA was sequenced. Second, the effect of GC bias was corrected using normalization of samples as well as LOESS and linear regressions. Finally, statistical analysis was performed after selecting a set of reference samples optimally adapted to a test sample from the whole reference samples. We evaluated our approach by performing cfDNA testing to assess the risk of trisomies 13, 18, and 21 using the sets of extracted reference samples. Results The adaptive selection algorithm presented here was used to choose a more optimized reference sample, which was evaluated by the coefficient of variation (CV), demonstrated a lower CV and higher sensitivity than standard approaches. Our adaptive approach also showed that fetal aneuploidies could be detected correctly by clearly splitting the z scores obtained for positive and negative samples. Conclusions We show that our adaptive reference selection algorithm for optimizing trisomy detection showed improved reliability and will further support practitioners in reducing both false negative and positive results

Sunshin Kim

HeeJung Jung

Sung Hee Han

SeungJae Lee

JeongSub Kwon

Min Gyun Kim

Hyungsik Chu

Kyudong Han

Hwanjong Kwak

Sunghoon Park

Hee Jae Joo

Minae An

Jungsu Ha

Kyusang Lee

Byung Chul Kim

Hailing Zheng

Xinqiang Zhu

Hongliang Chen

Jong Bhak

Directory of Open Access Journals

An adaptive detection method for fetal chromosomal aneuploidy using cell-free DNA from 447 Korean women

Background: Noninvasive prenatal testing (NIPT) using massively parallel sequencing of cell-free DNA (cfDNA) is increasingly being used to predict fetal chromosomal abnormalities. However, concerns over erroneous predictions which occur while performing NIPT still exist in pregnant women at high risk for fetal aneuploidy. We performed the largest-scale clinical NIPT study in Korea to date to assess the risk of false negatives and false positives using next-generation sequencing. Methods: A total of 447 pregnant women at high risk for fetal aneuploidy were enrolled at 12 hospitals in Korea. They underwent definitive diagnoses by full karyotyping by blind analysis and received aneuploidy screening at 11-22 weeks of gestation. Three steps were employed for cfDNA analyses. First, cfDNA was sequenced. Second, the effect of GC bias was corrected using normalization of samples as well as LOESS and linear regressions. Finally, statistical analysis was performed after selecting a set of reference samples optimally adapted to a test sample from the whole reference samples. We evaluated our approach by performing cfDNA testing to assess the risk of trisomies 13, 18, and 21 using the sets of extracted reference samples. Results: The adaptive selection algorithm presented here was used to choose a more optimized reference sample, which was evaluated by the coefficient of variation (CV), demonstrated a lower CV and higher sensitivity than standard approaches. Our adaptive approach also showed that fetal aneuploidies could be detected correctly by clearly splitting the z scores obtained for positive and negative samples. Conclusions: We show that our adaptive reference selection algorithm for optimizing trisomy detection showed improved reliability and will further support practitioners in reducing both false negative and positive results.ope

Kim, Sunshin

Jung, HeeJung

Han, Sung Hee

Lee, SeungJae

Kwon, JeongSub

Kim, Min Gyun

Chu, Hyungsik

Han, Kyudong

Kwak, Hwanjong

Park, Sunghoon

Joo, Hee Jae

An, Minae

Ha, Jungsu

Lee, Kyusang

Kim, Byung Chul

Zheng, Hailing

Zhu, Xinqiang

Chen, Hongliang

Bhak, Jong

ScholarWorks@UNIST

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