The following investigators are involved in Statistical Genetics projects, two examples of which are given below: David Umbach(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/umbach/index.cfm), Clarice Weinberg(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/weinberg/index.cfm), Dmitri Zaykin(http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/zaykin/index.cfm).
Mapping susceptibility haplotypes: Work has focused on statistical methods for localizing susceptibility genes for complex diseases and quantitative traits in humans. The power of association tests with di-allelic markers suffers if there are multiple susceptibility alleles. The Branch has shown that this loss of power can be reduced when the analysis is based on haplotypes, and have been developing general methods for linkage disequilibrium-based mapping of complex traits in outbred populations, incorporating information from multiple loci and alleles, with special emphasis on design and analysis of whole genome association scan experiments. Some of this work has involved methods for contrasting patterns of correlation between alleles at linked loci. Further work developed a method for identifying a risk-associated haplotype based on case-parent triad data with genotypes based on multiple linked single nucleotide polymorphisms (SNPs).
Hybrid design: Mapping of loci associated with risk of disease based on linkage disequilibrium can be accomplished using either a family-based, case-parent-triad approach or a population-based, case-control approach. The Branch proposed a hybrid design that offers greatly improved statistical power and flexibility, based on combining case-parent-triad genotype data with exposure data from unrelated population controls and genotype data from their parents.