We have developed an algorithm for combining data from multiple loci in a genomic survey to produce a vulnerability score; that may provide a quantitative index of genetic risk for an individual. This may help to predict within families who is likely to become ill, or may be used as a surrogate variable to help validate putative vulnerability markers. A sharing score is calculated for each allele at each locus for each pedigree based on the proportion of affected individuals within the pedigree that share that allele. Each individual in the pedigree then is assigned a locus vulnerability score combining the sharing scores for each allele and multiplying by a weighting factor indicating the relative value of that locus in the dataset. Locus vulnerability scores are added to give a total vulnerability score. This algorithm has been tested in a simulated dataset created for Genetic Analysis Workshop 12. Affecteds were successfully discriminated from unaffecteds though some of this discrimination appeared to be nonspecific when we looked at control loci. We have now modified the method and can achieve better discrimination with chosen loci than with control loci. In a separate analysis we have applied this method to the Genetics Initiative Bipolar dataset.
|Original language||English (US)|
|Number of pages||1|
|Journal||American Journal of Medical Genetics - Neuropsychiatric Genetics|
|State||Published - Oct 8 2001|
ASJC Scopus subject areas
- Psychiatry and Mental health
- Cellular and Molecular Neuroscience