Abstract
This chapter deals with a selection of medical applications of bioinformatics, starting with genetically based diseases and the use of single-nucleotide polymorphisms (SNPs). Infectious and noninfectious diseases are covered. The use of differentially expressed genes (DEGs) is outlined in a number of examples. Genome-wide association studies, as a possible approach to personalized medicine, are critically appraised. Gene-editing is briefly discussed. Automated diagnosis of disease is seen primarily as a problem of pattern recognition.
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Notes
- 1.
Some would also argue for a preventive role, but the primacy of the curative purpose is indisputable. See Ramsden (2021) for more discussion.
- 2.
- 3.
- 4.
Indeed, one could view the organism as a gigantic hidden Markov model (Sect. 17.5.2), in which the gene controls switching between physiological states via protein expression. Unlike the simpler models considered earlier, here the outputs could intervene in hidden layers.
- 5.
Since the physiological column includes entries for neurophysiological states, it might be tempting to continue the table by adding a column for the conscious experiences corresponding to the physiological and other entries. One must be careful to note, however, that conscious experience is in a different category from the entries in the columns that precede it (Ramsden 2001). Hence, correlation cannot be taken to imply identity (in the same way, a quadratic equation with two roots derived by a piece of electronic hardware is embodied in the hardware, but it makes no sense to say that the hardware has two roots, despite the fact that those roots have well-defined correlates in the electronic states of the circuit components).
- 6.
Mossink et al. (2012).
- 7.
These data can also be used to infer population structures (Jakobsson et al. 2008).
- 8.
These investigations are closely related to those of linkage disequilibrium (nonrandom association between alleles at different loci).
- 9.
Chumakov et al. (2005).
- 10.
There are about ten times more cells in the human microbiome than in the human body proper (cf. Chap. 19), but of course these cells are very small (and their individual genomes are much smaller than that of the human being) and their total mass only amounts to some 2% of human body mass. There is, however, an enormous variety of different microörganisms in the GIT.
- 11.
- 12.
- 13.
Shapiro (1992).
- 14.
An example of the lack of a simple genetic cause of disease is illustrated by the fact that the same mutations affecting the calcium channel protein in nerve cells are observed in patients whose symptoms range from sporadic headaches to partial paralysis lasting several weeks. This is further evidence in favour of Wright’s “many gene, many enzyme” hypothesis as opposed to Beadle and Tatum’s “one gene, one enzyme” idea.
- 15.
**ao et al. (2018).
- 16.
Zhou et al. (2018).
- 17.
Oh et al. (2011).
- 18.
Kim et al. (2013).
- 19.
Cross (2015).
- 20.
Many of these adverse reactions, which are closely related to susceptibility to toxins, can be traced to variation in an individual’s cytochrome P450 enzymes, which are strongly involved in drug metabolism (Zanger and Schwab 2013).
- 21.
These developments are generally referred to as pharmacogenomics.
- 22.
See Risch and Merikangas (1996) for more about the assumptions behind these formulae.
- 23.
Sander and Joung (2014).
- 24.
Conventional gene editing uses homologous recombination (HR). Plants are typically genetically modified (introduction of new genes, deletion of existing genes, alteration of existing genes) by transgenesis, which involves the introduction of a foreign gene into the plant’s genome using gene guns, bacterial vectors or viruses.
- 25.
Murry and Keller (2008).
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Ramsden, J. (2023). Medicine and Disease. In: Bioinformatics. Computational Biology. Springer, Cham. https://doi.org/10.1007/978-3-030-45607-8_26
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