A statistical method for scanning the genome for regions with rare disease alleles.
Studying the role of rare alleles in common disease has been prevented by the impractical task of determining the DNA sequence of large numbers of individuals. Next-generation DNA sequencing technologies are being developed that will make it possible for genetic studies of common disease to study the full frequency spectrum of genetic variation, including rare alleles. This report describes a method for scanning the genome for disease susceptibility regions that show an increased number of rare alleles among a sample of disease cases versus an ethnically matched sample of controls. The method was based on a hidden Markov model and the statistical support for a disease susceptibility region characterized by rare alleles was measured by a likelihood ratio statistic. Due to the lack of empirical data, the method was evaluated through simulation. The performance of the method was tested under the null and alternative hypotheses under a range of sequence generating and hidden Markov models parameters. The results showed that the statistical method performs well at identifying true disease susceptibility regions and that performance was primarily affected by the amount of variation in the neutral sequence and the number of rare disease alleles found in the disease susceptibility region.
Garner
Chad
C
Epidemiology Department, University of California, Irvine, California 92697-7550, USA. cgarner@uci.edu
D000483:Alleles; D016022:Case-Control Studies; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population; D015894:Genome, Human; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000379:methods*