High-throughput genomic technology in research and clinical management of breast cancer. Evolving landscape of genetic epidemiological studies

Candidate polymorphism-based genetic epidemiological studies have yielded little success in the search for low-penetrance breast cancer susceptibility genes. The lack of progress is partially due to insufficient coverage of genomic regions with genetic markers, as well as economic constraints, limiting both the number of genetic targets and the number of individuals being studied. Recent rapid advances in high-throughput genotyping technology and our understanding of genetic variation patterns across the human genome are now revolutionizing the way in which genetic epidemiological studies are being designed and conducted. Genetic epidemiological studies are quickly progressing from candidate gene studies to comprehensive pathway investigation and, further, to genomic epidemiological studies where the whole human genome is being interrogated to identify susceptibility alleles. This paper reviews the evolving approaches in the search for low-penetrance breast cancer susceptibility gene variants and discusses their potential promises and pitfalls.

[1]  H. Bush,et al.  Breast Cancer Research , 1978, British Journal of Cancer.

[2]  Daniel O Stram,et al.  Software for tag single nucleotide polymorphism selection , 2005, Human Genomics.

[3]  Douglas F. Easton,et al.  Association studies for finding cancer-susceptibility genetic variants , 2004, Nature Reviews Cancer.

[4]  J. Terwilliger,et al.  An utter refutation of the ‘Fundamental Theorem of the HapMap’ , 2006, European Journal of Human Genetics.

[5]  S. Coughlin,et al.  Genetic polymorphisms and risk of breast cancer. , 1999, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[6]  Douglas F. Easton,et al.  Polygenic susceptibility to breast cancer and implications for prevention , 2002, Nature Genetics.

[7]  Graham A. Colditz,et al.  The Nurses' Health Study: lifestyle and health among women , 2005, Nature Reviews Cancer.

[8]  Y. Coyle.  The Effect of Environment on Breast Cancer Risk , 2004, Breast Cancer Research and Treatment.

[9]  B. Ponder,et al.  A Systematic Review Of Genetic Polymorphisms and Breast Cancer Risk 1 , 2000 .

[10]  M. Daly,et al.  Genome-wide association studies for common diseases and complex traits , 2005, Nature Reviews Genetics.

[11]  J. Palmgren,et al.  Linkage Disequilibrium Mapping of CHEK2: Common Variation and Breast Cancer Risk , 2006, PLoS medicine.

[12]  N. Risch.  Searching for genetic determinants in the new millennium , 2000, Nature.

[13]  A. Syvänen.  Accessing genetic variation: genotyping single nucleotide polymorphisms , 2001, Nature Reviews Genetics.

[14]  N. Shikata,et al.  Dietary Factors Modifying Breast Cancer Risk and Relation to Time of Intake , 2005, Journal of Mammary Gland Biology and Neoplasia.

[15]  Malcolm C Pike,et al.  A comprehensive haplotype analysis of CYP19 and breast cancer risk: the Multiethnic Cohort. , 2003, Human molecular genetics.

[16]  Angel Carracedo,et al.  SNPs in forensic genetics: a review on SNP typing methodologies. , 2005, Forensic science international.

[17]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[18]  Daehee Kang,et al.  Genetic polymorphisms and cancer susceptibility of breast cancer in Korean women. , 2003, Journal of biochemistry and molecular biology.

[19]  M. King,et al.  Linkage of early-onset familial breast cancer to chromosome 17q21. , 1990, Science.

[20]  D. Hunter,et al.  Genetic Variations in XRCC2 and XRCC3 Are Not Associated with Endometrial Cancer Risk , 2004, Cancer Epidemiology Biomarkers & Prevention.

[21]  D. Hunter,et al.  Polymorphisms in DNA double-strand break repair genes and breast cancer risk in the Nurses' Health Study. , 2003, Carcinogenesis.

[22]  R. Adkins,et al.  Comparison of the accuracy of methods of computational haplotype inference using a large empirical dataset , 2004, BMC Genetics.

[23]  S. Chanock,et al.  Using germ-line genetic variation to investigate and treat cancer. , 2004, Drug discovery today.

[24]  Jason Cooper,et al.  Use of unphased multilocus genotype data in indirect association studies , 2004, Genetic epidemiology.

[25]  Paolo Vineis,et al.  A road map for efficient and reliable human genome epidemiology , 2006, Nature Genetics.

[26]  John Broxholme,et al.  A comparison of tagging methods and their tagging space. , 2005, Human molecular genetics.

[27]  P. McKeigue,et al.  Problems of reporting genetic associations with complex outcomes , 2003, The Lancet.

[28]  S. Gabriel,et al.  Efficiency and power in genetic association studies , 2005, Nature Genetics.

[29]  J. Landers,et al.  Using high-throughput SNP technologies to study cancer , 2006, Oncogene.

[30]  D. Hunter,et al.  Interaction between genetic variations in DNA repair genes and plasma folate on breast cancer risk. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[31]  S. Gabriel,et al.  The Structure of Haplotype Blocks in the Human Genome , 2002, Science.

[32]  B. Ponder,et al.  Common variation in EMSY and risk of breast and ovarian cancer: a case-control study using HapMap tagging SNPs , 2005, BMC Cancer.

[33]  D. Altshuler,et al.  Haplotype analysis of the HSD17B1 gene and risk of breast cancer: a comprehensive approach to multicenter analyses of prospective cohort studies. , 2006, Cancer research.