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Networks and Genetic Mapping

Genetic Mapping attempts to home in on the positions on the genome that are causative for a phenotype of interest such as disease susceptibility. The more that could be learnt about the genotypephenotype function, the more valuable this will be. The widespread redundancy in the genetic architecture presents a problem in this context, as many phenotypes will only be detectable by multiple events in different parts of the genomes. This could be solved by including more positions in the assumed genotypephenotype function. However, this immediately leads to a combinatorial explosions and a serious loss of statistical power. Examplifying with the human genome and focusing on standard genes, single gene genotypephenotype function would need to consider ~20.000 positions, considering only protein coding genes and that these are atomic. Two-gene functions would have to consider ~2*10 position pairs and even large numbers for characters depending on more genes. Clearly, a qualified reduction or weighting of which gene sets to consider, would be of value and could lead to a major boost in statistical power. Such a reduction could be achieved by the incorporation of biological knowledge and one kind of knowledge that is obtained on a large scale presently is the architecture of networks. The present project describes an idealized setup that could investigate potential benefits and problems of such approach. How could networks help with mapping? The object is to use network properties to map genes to characters and also make statements about which sets of genes will not work as a group and should not be considered as a combination in a mapping study. An ideal situation would be that the underlying network was known. This is almost true in a series of cases, where a universal metabolism is known and when the genome of the organism in question has been determined, then a sub-metabolism is defined. For any metabolism a set of characters that it defines should be listed. Clearly this is an ideal situation and in real applications the network for an organism is known with error and that should be included in the description.