Statistical Genetics and Genetic Epidemiology - Daniel J. Schaid

Project List

Quantitative methods for genetic epidemiology

The overall objectives are to facilitate analyses of complex genetic mechanisms by developing innovative statistical methods and software that can be used by biomedical researchers as outlined in our four specific aims:

1. Develop and evaluate probability models for haplotypes in order to improve our understanding of the complex structure of haplotypes in human populations and provide methods to account for ambiguous haplotypes when they are not directly observed due to unknown phase of diploid phenotypes;
2. Build statistical genetic models to evaluate the relative contribution of complex genetic mechanisms (haplotypes and metabolic pathways) and environmental risk factors to disease, as evaluated by standard case-control study designs;
3. The methods developed in Aims 1 and 2 will be extended to family-based study designs, including a hybrid design that combines the strengths of case-control and family-based designs, increasing power to detect genes of small effects;
4. Develop user-friendly software that implements our methods and make them widely available to biomedical researchers, including well-documented procedures and examples on their usage.

Quantitative methods for genetic linkage heterogeneity

The aims of this research project are to develop new quantitative methods for evaluation of genetic linkage heterogeneity based on recursive partitioning trees and regression models. Furthermore, user-friendly software will be developed and provided to the scientific community.

Localization of susceptibility loci in prostate cancer

The major aims of this study are to:

1. Localize genetic susceptibility loci in familial prostate cancer by genetic linkage analysis;
2. Produce a detailed physical map of identified regions; and
3. Analyze and characterize genes in the candidate regions to identify prostate cancer susceptibility genes.

Epidemiologic and genetic studies of breast cancer

A study that builds upon a unique resource of 426 four-to five-generation families ascertained through breast cancer patients originally ascertained between 1940 and 1952. The theme of this project is the interaction of genes and environment in the pathogenesis of this disease. A specific project that our laboratory is collaborating on is a genome-wide linkage study for genes related to breast density, a risk factor for breast cancer.

SPORE in prostate cancer: Genetic susceptibility in prostate cancer

The main aims of this research project are to test the hypothesis that common genetic polymorphisms for genes that encode enzymes involved in the androgen metabolic pathway, and genes that encode enzymes involved in estrogen and catecholestrogen formation, bioactivation and inactivation, are associated with 1) familial prostate cancer and 2) sporadic prostate cancer.

Prostate cancer susceptibility: The ICPCG Study

The aims of this multi-institution grant are to:

1. Pool results for prostate cancer families to determine evidence for linkage at previously identified loci and novel loci provided by the pool of all families;
2. Conduct linkage analyses in clinically relevant strata of the pooled data;
3. Develop new methodologies to facilitate and optimize aims (1) and (2).

Genetic epidemiology of prostate cancer: The Prostate Cancer Genetic Research Study (PROGRESS)

The aims of this project are to perform genetic linkage analysis of a variety of clinical and pathologic features of prostate cancer, as well as subsets of pedigrees defined by various other cancer histories. This PROGRESS study is conducted by Dr. Janet Stanford at the Fred Hutchinson Cancer Research Center, and our Mayo research lab is providing statistical analyses.

Pharmacogenetics of phase II drug metabolizing enzymes (Pharmacogenetic Research Network)

The primary focus of our lab for this pharmacogenetics research is to extend statistical methods and software that will be used to select haplotype-tagging SNPs (htSNPs), and provide statistical analyses of the associations of candidate genes (via haplotypes) with the traits measured in each of two clinical pharmacogenetic studies:

1. The role of genes related to response to aromatase treatment for breast cancer;
2. The role of genes related to response to treatment of major depression by escitalopram.