|Grant Number:||5R01CA122844-05 Interpret this number|
|Primary Investigator:||Douglas, Julie|
|Organization:||University Of Michigan|
|Project Title:||Mapping Genes for Mammographic Breast Density|
DESCRIPTION (provided by applicant): Increased mammographic breast density is one of the strongest independent predictors of breast cancer risk, yet perhaps the least understood. Family and twin studies provide compelling evidence for a substantial genetic influence on breast density. However, the specific genetic loci that contribute to the wide inter- individual variation in breast density are largely unknown. The overall objective of this proposal is to identify and localize the genetic loci and ultimately to characterize the genes that explain inter-individual variation in breast density using state-of-the-art mammography, new and objective density estimation tools and programs, and sophisticated molecular and statistical genetic methods in the Old Order Amish population of Lancaster County, Pennsylvania. The hypothesis underlying this proposal is that there exist genes with strong enough effects on breast density to be detected by linkage analysis. With relatively similar cultural and environmental experiences, a well-defined, genetically closed population structure, and extensive genealogical records, the Old Order Amish provide an ideal context in which to study the genetic contributions to breast density. The overall design of this proposal is the positional cloning of genes for breast density using related women (particularly sisters) from extended Amish families. The specific aims are to (1) recruit and characterize 1,200 Amish women with regard to breast density and factors known or suspected to modify breast density, (2) determine if the genetic and/or environmental contributions to breast density (and related traits) differ between pre- and post-menopausal women, (3) determine if the phenotypic correlation between breast density and related traits is mediated by the same genetic and/or environmental factors, (4) identify and localize loci for breast density through genome-wide quantitative trait linkage analyses utilizing a high-density map of -6,000 single nucleotide polymorphisms (SNPs), and (5) determine if chromosomal regions linked to variation in breast density are also linked to variation in factors known or suspected to modify breast density. The proposed research will likely result in the identification of one or more loci for breast density over the project period. Lessons learned from this research may provide important insights into the genetic etiology of breast density and its relationship with other breast cancer risk factors and ultimately inform future strategies for breast cancer prevention and control.
A method to prioritize quantitative traits and individuals for sequencing in family-based studies.
Authors: Shah KP, Douglas JA
Source: PLoS One, 2013;8(4), p. e62545.
EPub date: 2013 Apr 23.
Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms.
Authors: Zhou C, Wei J, Chan HP, Paramagul C, Hadjiiski LM, Sahiner B, Douglas JA
Source: Med Phys, 2010 May;37(5), p. 2289-99.
Dynamic multiple thresholding breast boundary detection algorithm for mammograms.
Authors: Wu YT, Zhou C, Chan HP, Paramagul C, Hadjiiski LM, Daly CP, Douglas JA, Zhang Y, Sahiner B, Shi J, Wei J
Source: Med Phys, 2010 Jan;37(1), p. 391-401.
Extent and distribution of linkage disequilibrium in the Old Order Amish.
Authors: Van Hout CV, Levin AM, Rampersaud E, Shen H, O'Connell JR, Mitchell BD, Shuldiner AR, Douglas JA
Source: Genet Epidemiol, 2010 Feb;34(2), p. 146-50.
Mammographic breast density--evidence for genetic correlations with established breast cancer risk factors.
Authors: Douglas JA, Roy-Gagnon MH, Zhou C, Mitchell BD, Shuldiner AR, Chan HP, Helvie MA
Source: Cancer Epidemiol Biomarkers Prev, 2008 Dec;17(12), p. 3509-16.
EPub date: 2008 Nov 24.
PedMine--a simulated annealing algorithm to identify maximally unrelated individuals in population isolates.
Authors: Douglas JA, Sandefur CI
Source: Bioinformatics, 2008 Apr 15;24(8), p. 1106-8.
EPub date: 2008 Mar 5.