Human Population Genetics

Analysis and Exploration of the Use of Rule-based Algorithms and Consensus Methods for the Inferral of Haplotypes

Steven Orzack, Senior Research Scientist
(with Dan Gusfield, of University of California, Davis, and Vincent P. Stanton, Jr.)

The difficulty of experimental determination of haplotypes from phase-unknown genotypes has stimulated the development of nonexperimental inferral methods. One well-known approach for a group of unrelated individuals involves using the trivially deducible haplotypes (those found in individuals with zero or one heterozygous sites) and a set of rules to infer the haplotypes underlying ambiguous genotypes (those with two or more heterozygous sites). Neither the manner in which this "rule-based" approach should be implemented nor the accuracy of this approach have been adequately assessed. We implemented eight variations of this approach that differed in how a reference list of haplotypes was derived and in the rules for the analysis of ambiguous genotypes. We assessed the accuracy of these variations by comparing predicted and experimentally determined haplotypes involving nine polymorphic sites in the human apolipoprotein E (APOE) locus. The eight variations resulted in substantial differences in the average number of correctly inferred haplotype pairs. More than one set of inferred haplotype pairs was found for each of the variations we analyzed, implying that the rule-based approach is not sufficient by itself for haplotype inferral, despite its appealing simplicity. Accordingly, we explored consensus methods in which multiple inferrals for a given ambiguous genotype are combined so as to generate a single inferral; we show that the set of these "consensus" inferrals for all ambiguous genotypes is more accurate than the typical single set of inferrals chosen at random. We also use a consensus prediction to divide ambiguous genotypes into those whose algorithmic inferral is certain or almost certain and those whose less certain inferral makes molecular inferral preferable.

This project is partially supported by the National Science Foundation.

Genetic Studies of Substance Abuse in Iowa Adoptees

Steven Orzack, Senior Research Scientist
(with Robert Philibert, of the University of Iowa)

We are investigating the effects of genetic variability and gene-environment interactions on the tendency to abuse Nicotine, Alcohol, and Marijuana. Our study population is composed of Iowa adoptees. The identification of these effects will help us develop more effective biological and environmental treatments for Substance Use Disorders.

This project is partially supported by the National Institutes of Health.