Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: From genotype to EEG endophenotype: a route for post-genomic understanding of complex psychiatric disease? | de Geus Genome Medicine 2010 2 63 http content 2 9 63 w Genome Medicine COMMENTARY L__ From genotype to EEG endophenotype a route for post-genomic understanding of complex psychiatric disease Eco JC de Geus - 2-3 Abstract Twin and family studies have shown the importance of biological variation in psychiatric disorders. Heritability estimates vary from 50 to 80 for cognitive disorders such as schizophrenia- attention deficit hyperactivity disorder and autism and from 40 to 65 for affective disorders such as major depression- anxiety disorders and substance abuse. Pinpointing the actual genetic variants responsible for this heritability has proven difficult- even in the recent wave of genome-wide association studies. Brain endophenotypes derived from electroencephalography EEG have been proposed as a way to support gene-finding efforts. A variety of EEG and event-related-potential endophenotypes are linked to psychiatric disorders- and twin studies have shown a striking genetic contribution to these endophenotypes. However- the clear need for very large sample sizes strongly limits the usefulness of EEG endophenotypes in gene-finding studies. They require extended laboratory recordings with sophisticated and expensive equipment that are not amenable to epidemiology-scaled samples. Instead- EEG endophenotypes are far more promising as tools to make sense of candidate genetic variants that derive from association studies existing clinical data from patients or questionnaire-based assessment of psychiatric symptoms in the population at large are better suited for the association studies themselves. EEG endophenotypes can help us understand where in the brain- in which stage and during what type of information processing these genetic variants have a role. Such testing can be done in the more modest samples that are feasible for EEG research. With increased understanding of how genes affect the brain- combinations of genetic risk scores and .
