Perlegen Sciences

Mountain View, CA, United States

Perlegen Sciences

Mountain View, CA, United States
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Huang Y.,Columbia University | Hinds D.A.,Perlegen Sciences | Qi L.,University of California at Davis | Prentice R.L.,Fred Hutchinson Cancer Research Center
Genetic Epidemiology | Year: 2010

We examine the measurement properties of pooled DNA odds ratio estimates for 7,357 single nucleotide polymorphisms (SNPs) genotyped in a genome-wide association study of postmenopausal breast cancer. This study involved DNA pools formed from 125 cases or125 matched controls. Individual genotyping for these SNPs subsequently came available for a substantial majority of women included in seven pool pairs, providing the opportunity for a comparison of pooled DNA and individual odds ratio estimates and their variances. We find that the "per minor allele" odds ratio estimates from the pooled DNA comparisons agree fairly well with those from individual genotyping. Furthermore, the log-odds ratio variance estimates support a pooled DNA measurement model that we previously described, although with somewhat greater extra-binomial variation than was hypothesized in project design. Implications for the role of pooled DNA comparisons in the future genetic epidemiology research agenda are discussed. © 2010 Wiley-Liss, Inc.

Prentice R.L.,Fred Hutchinson Cancer Research Center | Huang Y.,Fred Hutchinson Cancer Research Center | Hinds D.A.,Perlegen Sciences | Peters U.,Fred Hutchinson Cancer Research Center | And 6 more authors.
Cancer Epidemiology Biomarkers and Prevention | Year: 2010

Background: The Women's Health Initiative dietary modification (DM) trial provided suggestive evidence of a benefit of a low-fat dietary pattern on breast cancer risk, with stronger evidence among women whose baseline diet was high in fat. Single nucleotide polymorphisms (SNP) in the FGFR2 gene relate strongly to breast cancer risk and could influence intervention effects. Methods: All 48,835 trial participants were postmenopausal and ages 50 to 79 years at enrollment (1993-1998). We interrogated eight SNPs in intron 2 of the FGFR2 gene for 1,676 women who developed breast cancer during trial follow-up (1993-2005). Case-only analyses were used to estimate odds ratios for the DM intervention in relation to SNP genotype. Results: Odds ratios for the DM intervention did not vary significantly with the genotype for any of the eight FGFR2 SNPs (P ≥ 0.18). However, odds ratios varied (P < 0.05) with the genotype of six of these SNPs, among women having baseline percent of energy from fat in the upper quartile (≥36.8%). This variation is most evident for SNP rs3750817, with odds ratios for the DM intervention at 0, 1, and 2 minor SNP alleles of 1.06 [95% confidence intervals (95% CI), 0.80-1.41], 0.53 (95% CI, 0.38-0.74), and 0.62 (95% CI, 0.33-1.15). The nominal significance level for this interaction is P = 0.005, and P = 0.03 following multiple testing adjustment, with most evidence deriving from hormone receptor-positive tumors. Conclusion: Invasive breast cancer odds ratios for a low-fat dietary pattern, among women whose usual diets are high in fat, seem to vary with SNP rs3750817 in the FGFR2 gene. ©2010 AACR.

Huang Y.,Fred Hutchinson Cancer Research Center | Ballinger D.G.,Perlegen Sciences | Dai J.Y.,Fred Hutchinson Cancer Research Center | Peters U.,Fred Hutchinson Cancer Research Center | And 8 more authors.
Genome Medicine | Year: 2011

Background: Genome-wide association studies have identified several genomic regions that are associated with breast cancer risk, but these provide an explanation for only a small fraction of familial breast cancer aggregation. Genotype by environment interactions may contribute further to such explanation, and may help to refine the genomic regions of interest.Methods: We examined genotypes for 4,988 SNPs, selected from recent genome-wide studies, and four randomized hormonal and dietary interventions among 2,166 women who developed invasive breast cancer during the intervention phase of the Women's Health Initiative (WHI) clinical trial (1993 to 2005), and one-to-one matched controls. These SNPs derive from 3,224 genomic regions having pairwise squared correlation (r2) between adjacent regions less than 0.2. Breast cancer and SNP associations were identified using a test statistic that combined evidence of overall association with evidence for SNPs by intervention interaction.Results: The combined 'main effect' and interaction test led to a focus on two genomic regions, the fibroblast growth factor receptor two (FGFR2) and the mitochondrial ribosomal protein S30 (MRPS30) regions. The ranking of SNPs by significance level, based on this combined test, was rather different from that based on the main effect alone, and drew attention to the vicinities of rs3750817 in FGFR2 and rs7705343 in MRPS30. Specifically, rs7705343 was included with several FGFR2 SNPs in a group of SNPs having an estimated false discovery rate < 0.05. In further analyses, there were suggestions (nominal P < 0.05) that hormonal and dietary intervention hazard ratios varied with the number of minor alleles of rs7705343.Conclusions: Genotype by environment interaction information may help to define genomic regions relevant to disease risk. Combined main effect and intervention interaction analyses raise novel hypotheses concerning the MRPS30 genomic region and the effects of hormonal and dietary exposures on postmenopausal breast cancer risk. © 2011 Huang et al.; licensee BioMed Central Ltd.

Mealiffe M.E.,Perlegen Sciences | Stokowski R.P.,Perlegen Sciences | Rhees B.K.,Perlegen Sciences | Prentice R.L.,Fred Hutchinson Cancer Research Center | And 2 more authors.
Journal of the National Cancer Institute | Year: 2010

BackgroundThe Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.MethodsSeven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.ResultsThe SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P <. 001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10-5). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10 -5).ConclusionsCombining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk. © 2010 The Author.

Wessel J.,SRI International | Wessel J.,Indiana University | McDonald S.M.,SRI International | Hinds D.A.,Perlegen Sciences | And 13 more authors.
Neuropsychopharmacology | Year: 2010

Common single-nucleotide polymorphisms (SNPs) at nicotinic acetylcholine receptor (nAChR) subunit genes have previously been associated with measures of nicotine dependence. We investigated the contribution of common SNPs and rare single-nucleotide variants (SNVs) in nAChR genes to Fagerström test for nicotine dependence (FTND) scores in treatment-seeking smokers. Exons of 10 genes were resequenced with next-generation sequencing technology in 448 European-American participants of a smoking cessation trial, and CHRNB2 and CHRNA4 were resequenced by Sanger technology to improve sequence coverage. A total of 214 SNP/SNVs were identified, of which 19.2% were excluded from analyses because of reduced completion rate, 73.9% had minor allele frequencies 5%, and 48.1% were novel relative to dbSNP build 129. We tested associations of 173 SNP/SNVs with the FTND score using data obtained from 430 individuals (18 were excluded because of reduced completion rate) using linear regression for common, the cohort allelic sum test and the weighted sum statistic for rare, and the multivariate distance matrix regression method for both common and rare SNP/SNVs. Association testing with common SNPs with adjustment for correlated tests within each gene identified a significant association with two CHRNB2 SNPs, eg, the minor allele of rs2072660 increased the mean FTND score by 0.6 Units (P0.01). We observed a significant evidence for association with the FTND score of common and rare SNP/SNVs at CHRNA5 and CHRNB2, and of rare SNVs at CHRNA4. Both common and/or rare SNP/SNVs from multiple nAChR subunit genes are associated with the FTND score in this sample of treatment-seeking smokers. © 2010 Nature Publishing Group All rights reserved.

Tian C.,Perlegen Sciences | Stokowski R.P.,Perlegen Sciences | Kershenobich D.,National Autonomous University of Mexico | Ballinger D.G.,Perlegen Sciences | And 2 more authors.
Nature Genetics | Year: 2010

Two genome-wide association studies (GWAS) have described associations of variants in PNPLA3 with nonalcoholic fatty liver and plasma liver enzyme levels. We investigated the contributions of these variants to liver disease in Mestizo subjects with a history of alcohol dependence. We found that rs738409 in PNPLA3 is strongly associated with alcoholic liver disease and clinically evident alcoholic cirrhosis (unadjusted OR= 2.25, P=1.7 × 10-10 ancestry-adjusted OR=1.79, P=1.9 × 10 -5). © 2010 Nature America, Inc. All rights reserved.

Perlegen Sciences and University of Cambridge | Date: 2015-06-12

Correlations between polymorphisms and breast cancer are provided. Methods of diagnosing, prognosing, and treating breast cancer are provided. Systems and kits for diagnosis, prognosis and treatment of breast cancer are provided. Methods of identifying breast cancer modulators are also described.

Perlegen Sciences | Date: 2011-04-27

Methods of treating an individual exhibiting a medical condition are disclosed. The methods involve determining a score of an individual based on the individuals genotypic information, comparing the score to at least one threshold value, wherein the result of the comparison is indicative of a beneficial response to a treatment, and providing a suitable treatment to the individual.

News Article | November 12, 2007

Imagine a world in which most of the treatments offered by doctors work flawlessly because they can order lab tests that somewhat accurately forecast which therapies will be effective. This is called personalized medicine, and it is not quite here yet, but it is certainly on the way. Today at the University of California San Francisco Conference Center, hundreds of key players in the biotech industry gathered to discuss their vision for the near future. I attended the talks and spoke to some of the panelists. The meeting began with a welcome from Caroline Kovac, managing director of Burrill and Company, a large investment and media firm. My favorite part: she showed a graph of how frequently two industry buzzwords popped up in the news. According to Google, both have been on the rise. Kovac was followed by her boss, Steven Burrill, leader of the company that bears his name. He gave a passionate PowerPoint presentation that focused heavily on the bright financial prospects of the biotech industry. His optimistic overview was followed by three intense discussions. Doctors, academics, a regulatory official, and several chief executive officers weighed in on the obstacles that stand before their common dream. Almost everyone seemed to be on the same page. In short: Early medical testing and treatment could save patients and healthcare providers a ton of money, but nobody wants to pay for unproven and often expensive new lab work. FDA approval is not required for laboratory tests, but it is an indicator that products are actually beneficial to doctors and patients. There is a severe lack of communication between the companies that design personalized medicine products and the companies that are reluctant to pay for them. Brad Margus, Executive Vice Chairman of Perlegen Sciences received a round of applause for his suggestion that the payors make a list of the top ten products (biomarker tests) that they would support. Biomarkers, the genes, proteins, and other things that personalized medicine products measure, can initially appear to be strong signposts that forecast how a disease or treatment will work out, but many of the sophisticated scientific indicators prove worthless to doctors. Several of the panelists referred to markers that are truly useful and easy to identify as "low-hanging fruit", which seem to be rare commodities in the real world. Cancer genetics is the most active arena for new products. Franklyn Prendergast, director of the Center for Individualized Medicine Research at the Mayo Clinic called it "the posterchild of personalized medicine." Pioneering companies like Genomic Health are steadily focused on that market. I will follow up with several detailed posts once the meeting has ended tomorrow. In the meantime, what would you like to know about personalized medicine?

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