



Since the beginnings of the Human Genome Project, scientists have promised that knowledge of all of our genes would enable a deeper understanding of heritable diseases, a promise currently being fulfilled using bioinformatics approaches. Millions of dollars of research have been spent on finding protein-coding genes, our place in molecular evolution, and polymorphic variations—differences in DNA between members of a population—that are commonly found in patients with a disease but not in healthy individuals. In such projects, groups of patients and healthy participants have their DNA analysed, looking for variations between their DNA sequences. After statistical analysis, changes in the DNA sequence that commonly appear in the patients but rarely in the healthy participants are associated with the disease of interest, and may be the causes of the disease. These genome-wide association studies have discovered thousands of polymorphisms associated with dozens of diseases using sophisticated programming and statistics [6]. It should be noted though that these are merely statistical associations with diseases, and that these research projects usually do not interrogate the whole genome. In addition, understanding of the functions of these polymorphisms is still wanting.

While polymorphisms may be associated with diseases, high-throughput sequencing approaches coupled with bioinformatics tools are homing in on the exact genetic causes of such diseases including atypical hemolytic-uremic syndrome [7], hereditary hypertension [8], and autism [9]. These studies usually begin by sequencing parts of or entire genomes and transcriptomes (the sum of all DNA transcripts) via whole-genome, RNA, or exome (the part of the genome formed by exons) sequencing. By comparing patient genomes with a reference genome, researchers can pinpoint base-pair differences between patients and what is expected. If the variations are in exons, their impact on the protein can be predicted. In other cases, binding sites for other proteins may be created by the change in the genetic sequence, causing unusual, and harmful levels of transcription [10]. Using bioinformatics researchers can create a list of the more likely “suspects” from the thousands of polymorphisms that differ between patients with a disease and healthy controls, making the problem of finding the functional genetic determinants of diseases more tractable.

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