1.5Mb deletion of chromosome 4p16.3 associated with postnatal growth delay, psychomotor impairment, epilepsy, impulsive behavior and asynchronous skeletal development.

Several Wolf-Hirschhorn syndrome patients have been studied, mouse models for a few candidate genes have been constructed and two WHS critical regions have been postulated, but the molecular basis of the syndrome remains poorly understood. Single gene contributions to phenotypes of microdeletion syndromes have often been based on the study of patients carrying small, atypical deletions. We report a 5-year-old girl harboring an atypical 1.5Mb del4p16.3 and review seven previously published patients carrying a similar deletion. They show a variable clinical presentation and the only consistent feature is post-natal growth delay. However, four of eight patients carry a ring (4), and ring chromosomes in general are associated with growth deficiency. The Greek helmet profile is absent, although a trend towards common dysmorphic features exists. Variable expressivity and incomplete penetrance might play a role in WHS, resulting in difficult clinical diagnosis and challenge in understanding of the genotype/phenotype correlation.

No pain relief from codeine...? An introduction to pharmacogenomics.

Drug treatment remains a mainstay of medicine. In some situations a drug unexpectedly has no effect, or unforeseen serious side effects occur. For the patient this represents a dangerous and potentially life-threatening situation. It certainly is a distressing experience for the doctor. At the societal level, adverse drug reactions represent a leading cause of disease and death. Genetic variation often underlies these unexpected situations. Pharmacogenetics is the term used about genetically determined variability in the metabolism of drugs. Pharmacogenomics usually refers to drug discovery based on knowledge of genes, but it is a discipline that offers insight into aetiologic mechanisms, and possible prevention and treatment. There is a trend towards a definition of pharmacogenomics that includes both pharmacogenetics and pharmacogenomics as defined above. Our article is an introduction to pharmacogenomics, using the broader definition. Biotechnological methods cannot be understood without a grasp of basic medical genetics, and we provide a brush-up on the fundamentals. We then outline pharmacogenetics, giving examples of genetically based variation in drug metabolising enzymes, drug receptors and drug transporting proteins. Modern biotechnology would be unthinkable without the aid of computers, and we briefly touch upon the field of bioinformatics. Finally, we give an overview of pharmacogenomics in the narrower sense. The rapidly growing field of pharmacogenomics is going to influence our everyday practice of medicine in the immediate future.

The genetic algorithm applied to haplotype data at the LDL receptor locus.

Conventional statistical methods based upon single restriction fragment length polymorphisms often prove inadequate in studies of genetic variation. Cladistic analysis has been suggested as an alternative, but requires basic assumptions that usually cannot be met. We wanted to test whether it could be a workable approach to apply the genetic algorithm, an artificial intelligence method, to haplotype data. The genetic algorithm creates in-computer artificial 'individuals', all having 'genes' coding for solutions to a problem. The individuals are allowed to compete and 'mate', individuals with genes coding for better solutions mating more often. Genes coding for good solutions survive through generations of the genetic algorithm. At the end of the run, the best solutions can be extracted. We applied the genetic algorithm to data consisting of cholesterol values and haplotypes made up of seven restriction sites at the LDL receptor locus. The persons included were 114 FH (familial hypercholesterolemia) patients and 61 normals. The genetic algorithm found the restriction sites 1 (Sph1 in intron 6), 2 (StuI in exon 8), and 7 (ApaLI site in the 3' flanking region) were associated with high cholesterol levels. As a validity check we used runs of the genetic algorithm applied to 'artificial patients', i.e. artificially generated haplotypes linked to artificially generated cholesterol values. This demonstrated the genetic algorithm consistently found the appropriate haplotype. We conclude that the genetic algorithm may be a useful tool for studying genetic variation.

Artificial intelligence in pediatrics: important clinical signs in newborn syndromes.

New methods are warranted in the field of syndromology. This study is an exploration into whether an artificial intelligence method (ID3) could provide a new angle for approaching syndromes. Diagnosing syndromes in the newborn is difficult. The accepted approach is to look for individual clinical signs that add up to a syndrome diagnosis. Of all possible clinical signs, one would want to extract the signs with the strongest predictive power. I used the ID3 algorithm to extract predictive clinical signs from a catalogue of syndromes (Birth Defects Encyclopedia Online; BDEO). Using information from BDEO, files of randomly generated "patients" were created. The signs consistently high in the identification tree were long philtrum, short palpebral fissures, low-set ears, and hepatosplenomegaly. The program used featured a crude "expert system" based on the ID3 algorithm. When using one-half of the data set as a training set and the other half as a testbed, a correct classification rate of 92.1-98.1% was attained. When the ID3 expert system was tested against cases from a clinical database (Pictures of Standard Syndromes and Undiagnosed Malformations), the correct classification rate was less than 20%. This may not necessarily reflect faults with the ID3 approach, but possibly biases in the clinical database. In syndromology no "criterion standards" exist that can confirm a diagnosis. The statistical method of cluster analysis does not require prior knowledge of diagnoses and will make a tree of syndromes based upon clinical signs. A cluster analysis was performed as a validity check to provide a tree for comparison with the ID3 tree. There was a reasonable degree of agreement between the two. Applying artificial intelligence methods to this field highlights problems with basic assumptions and philosophical aspects of syndrome diagnosis.