Seattle scientists are planning to study the Major Histocompatibility Complex (MHC) of the human genome. They have developed a new method for doing the same. MHC is a large region, found on chromosome 6. It encodes more than 400 known genes, the HLA genes governs tissue type and participate in the immune system by protecting people from infection or by governing susceptibility to autoimmune diseases or cancer. The MHC is one the most diverse regions of the human genome and its diversity are thought to have been shaped by widely varying evolutionary forces. The procedure is described in the Proceedings of the National Academy of Sciences.
This method is an efficient way to map genes in the MHC that are responsible for many human diseases, and might also be useful in studying other gene complexes that have a lot of variability. The researchers are Dr. Zhen Guo and Dr. Mari Malkki of the Division of Clinical Research at the Fred Hutchinson Cancer Research Institute (FHCRC) in Seattle, Dr. Leroy Hood of Seattle's Institute for Systems Biology, and Dr. Effie Petersdorf of the Division of Clinical Reseearch at FHCRC and the Division of Medical Oncology, Department of Medicine at the University of Washington.
AdvertisementMHC genes have remained unchanged throughout human evolution. MHC also governs the degree of people's acceptance or rejection of transplanted organs or bone marrow transplants. Identical twins, for example, have identical MCH genes and therefore can receive transplants from each other without risk of rejection. MHC segments are thought to be inherited as an entire block, called a haplotype rather than as separate genes. Haplotypes may be one of the genetic reasons behind complex diseases that are not associated with just one gene or one genetic mutation, but with sets of genes. Researchers plan to expand their method to span the entire MHC, but this would require reconstructing the huge complex into several overlapping segments. This new lab method would help the researchers to conduct genetic studies in populations of unrelated individuals.