Bioinformatics Analyses for Next-Generation Sequencing of Plasma DNA

The presence of fetal DNA in the cell-free plasma of pregnant women was first described in 1997. The initial clinical applications of this phenomenon focused on the detection of paternally inherited traits such as sex and rhesus D blood group status. The development of massively parallel sequencing technologies has allowed more sophisticated analyses on circulating cell-free DNA in maternal plasma. For example, through the determination of the proportional representation of chromosome 21 sequences in maternal plasma, noninvasive prenatal diagnosis of fetal Down syndrome can be achieved with an accuracy of >98%. In the first part of my thesis, I have developed bioinformatics algorithms to perform genome-wide construction of the fetal genetic map from the massively parallel sequencing data of the maternal plasma DNA sample of a pregnant woman. The construction of the fetal genetic map through the maternal plasma sequencing data is very challenging because fetal DNA only constitutes approximately 10% of the maternal plasma DNA. Moreover, as the fetal DNA in maternal plasma exists as short fragments of less than 200 bp, existing bioinformatics techniques for genome construction are not applicable for this purpose. For the construction of the genome-wide fetal genetic map, I have used the genome of the father and the mother as scaffolds and calculated the fractional fetal DNA concentration. First, I looked at the paternal specific sequences in maternal plasma to determine which portions of the father's genome had been passed on to the fetus. For the determination of the maternal inheritance, I have developed the Relative Haplotype Dosage (RHDO) approach. This method is based on the principle that the portion of maternal genome inherited by the fetus would be present in slightly higher concentration in the maternal plasma. The use of haplotype information can enhance the efficacy of using the sequencing data. Thus, the maternal inheritance can be determined with a much lower sequencing depth than just looking at individual loci in the genome. This algorithm makes it feasible to use genome-wide scanning to diagnose fetal genetic disorders prenatally in a noninvasive way.;As the emergence of targeted massively parallel sequencing, the sequencing cost per base is reducing dramatically. Even though the first part of the thesis has already developed a method to estimate fractional fetal DNA concentration using parental genotype informations, it still cannot be used to deduce the fractional fetal DNA concentration directly from sequencing data without prior knowledge of genotype information. In the second part of this thesis, I propose a statistical mixture model based method, FetalQuant, which utilizes the maximum likelihood to estimate the fractional fetal DNA concentration directly from targeted massively parallel sequencing of maternal plasma DNA. This method allows fetal DNA concentration estimation superior to the existing methods in term of obviating the need of genotype information without loss of accuracy. Furthermore, by using Bayes' rule, this method can distinguish the informative SNPs where mother is homozygous and fetus is heterozygous, which is potential to detect dominant inherited disorder.;Besides the genetic analysis at the DNA level, epigenetic markers are also valuable for noninvasive diagnosis development. In the third part of this thesis, I have also developed a bioinformatics algorithm to efficiently analyze genomewide DNA methylation status based on the massively parallel sequencing of bisulfite-converted DNA. DNA methylation is one of the most important mechanisms for regulating gene expression. The study of DNA methylation for different genes is important for the understanding of the different physiological and pathological processes. Currently, the most popular method for analyzing DNA methylation status is through bisulfite sequencing. The principle of this method is based on the fact that unmethylated cytosine residues would be chemically converted to uracil on bisulfite treatment whereas methylated cytosine would remain unchanged. The converted uracil and unconverted cytosine can then be discriminated on sequencing. With the emergence of massively parallel sequencing platforms, it is possible to perform this bisulfite sequencing analysis on a genome-wide scale. However, the bioinformatics analysis of the genome-wide bisulfite sequencing data is much more complicated than analyzing the data from individual loci. Thus, I have developed Methyl-Pipe, a bioinformatics program for analyzing the DNA methylation status of genome-wide methylation status of DNA samples based on massively parallel sequencing. In the first step of this algorithm, an in-silico converted reference genome is produced by converting all the cytosine residues to thymine residues. Then, the sequenced reads of bisulfite-converted DNA sequences are aligned to this modified reference sequence. Finally, post-processing of the alignments removes non-unique and low-quality mappings and characterizes the methylation pattern in genome-wide manner. Making use of this new program, potential fetal-specific hypomethylated regions which can be used as blood biomarkers can be identified in a genome-wide manner.