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Noninvasive Fetal Trisomy (NIFTY) test: an advanced noninvasive prenatal diagnosis methodology for fetal autosomal and sex chromosomal aneuploidies

Fuman Jiang1, Jinghui Ren2, Fang Chen1, Yuqiu Zhou3, Jiansheng Xie4, Shan Dan5, Yue Su5, Jianhong Xie3, Baomin Yin3, Wen Su3, Huakun Zhang4, Wei Wang1, Xianghua Chai1, Linhua Lin2, Hui Guo2, Qiyun Li2, Peipei Li1, Yuying Yuan1, Xiaoyu Pan1, Yihan Li1, Lifu Liu1, Huifei Chen1, Zhaoling Xuan1, Shengpei Chen1, Chunlei Zhang1, Hongyun Zhang1, Zhongming Tian1, Zhengyu Zhang1, Hui Jiang1, Lijian Zhao1, Weimou Zheng1, Songgang Li1, Yingrui Li1, Jun Wang1, Jian Wang1 and Xiuqing Zhang1*

Author Affiliations

1 BGI- Shenzhen, Shenzhen, China

2 The Center of Prenatal Diagnosis, Shenzhen People’s Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, Guangdong, China

3 Zhuhai Institute of Medical Genetics, Zhuhai Municipal Maternal and Child Healthcare Hospital, Zhuhai, China

4 Central for Prenatal Diagnosis, Shenzhen Maternity and Child Healthcare Hospital, Affiliated Southern Medical University, Shenzhen, China

5 Department of Perinatology, Beijing Obstetrics and Gynecology Hospital-Capital University of Medical Sciences, Beijing, China

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BMC Medical Genomics 2012, 5:57  doi:10.1186/1755-8794-5-57

Published: 1 December 2012

Additional files

Additional file 1:

Figure S1. The correlation between sequence GC content and relative k-mer coverage. We plotted the relative k-mer coverage of each chromosome (y-axis) among our 300 controls against the corresponding sequence GC content (x-axis). Red plot are for female fetuses and black plot are for male fetuses.

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Additional file 2:

Figure S2. The tendency between normalized k-mer coverage and corresponding GC content. The GC content of each chromosome is listed in orders. The blue bars refer to the chromosomes that have a negative correlation between the k-mer coverage and the GC content, green bar refer chromosomes that have a positive correlation between the k-mer coverage and the GC content, and yellow bar refer to the chromosomes with no correlation.

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Additional file 3:

Figure S3. The reconstructed relationship between chromosomes by GC content. We reconstructed the GC-content relationship between the different chromosomes by clustering the 35-mer counts for 36 GC levels (y-axis) on the different chromosomes (x-axis). The normalized 35-mer counts, as a percentage of each chromosome, are color-coded in the heat map.

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Additional file 4:

Figure S4. The relationship between estimated cff-DNA concentration and gestational week. The black dots represent the cff-DNA concentrations (y-axis) plotted against the corresponding gestational week (x-axis) for the 443 samples with male fetuses.

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Additional file 5:

Figure S5. The standard deviation and sample numbers. The standard deviations of the difference between the observed and fitted k-mer coverage (y-axis) for different numbers of samples (x-axis). Different chromosomes are colour-coded. The standard deviation becomes stable when the number of samples is larger than 100.

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Additional file 6:

Figure S6. The relationship between tags number and the standard deviation of relative k-mer coverage among 150 samples. The standard deviations of the relative k-mer coverage (y-axis) declines with the increasing number of tags (x-axis) from 0.5 to 3.5 million for each chromosome.

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Additional file 7:

Figure S7. The aneuploidy detection power estimation. The colored contour lines show the aneuploidy detection power at different gestational weeks (x-axis) and with different numbers of unique reads (y-axis).Fetal genders are shown separately. The power is much higher when the fetus is male.

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