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The 18th Annual International Conference on Intelligent Systems for Molecular Biology () ended today, wrapping up three days of lectures and posters presented by some of the world’s finest computational and molecular biologists, mathematicians, computer scientists and statisticians.  

David Altshuler, Director of the Ó³»­´«Ã½â€™s Program in Medical and Population Genetics, gave the final keynote address this morning.  Speaking from his unique perspective as a physician who entered the field of genomics to examine the genetic basis for human disease, Altshuler gave his insights into the past, present and future of the field.

Altshuler reminded the audience that the field of genetic inheritance has undergone rapid evolution over the last century, with predominating theories of inheritance hypothesized, tested, refuted, confirmed, and, even, combined.  Joining of the principles of Mendelian genetics – phenotypic traits are inherited as single identifiable agents – with the chromosome theory of inheritance – chromosomes within cells are the carriers of inheritable traits – created the core of classical genetics.  The notion that multiple genes could affect a phenotypic outcome (i.e. a complex trait) was integrated into classical genetics, suggesting inheritance does not necessarily follow Mendelian patterns.

Similar to inheritance theories, the tools to examine genetic inheritance have clearly evolved over the last century.  Altshuler explained that genetic mapping – the process of assigning DNA loci and genes to chromosomes – was once much more laborious. Disease-causing genes that follow the simple rules of Mendelian inheritance (i.e. Huntington’s Disease) took as much as 10 years to identify before the application of restriction fragment length polymorphism (RFLP) to the human genome in the 1990s.  While RFLP allowed for the identification of many disease-causing genes following Mendelian inheritance patterns, it failed to pinpoint genetic links for common diseases, which is often conferred by variations in multiple genes.

In our current era of genomic research, characterized by the HapMap Project and 1000 Genomes Project, researchers are identifying the genetic underpinnings of common diseases, such as type 2 diabetes, asthma and Crohn’s disease. In the coming years, scientists expect more than 99.9 percent of all human genetic variation to be identified, providing an increasingly robust database for genome-wide association studies (GWAS).  

Beyond associating genes with a disease, researchers are establishing causal links between genes and diseases, creating a scientific environment that’s ripe for explaining the underlying biology of common diseases. Altshuler added that integration of genomic resources, bioinformatic tools and molecular experiments is essential for relating human genotype to phenotype and common diseases over the next five years.