DNA repair SNPs Associated with Breast Cancer By: Brittany Duncan Mentors: Janet Sinsheimer PhD (UCLA) Mary Sehl M.D.(UCLA) What We Aim to Do To ultimately determine: What SNP and Environmental factors contribute to breast cancer Whether a combination of SNPs acting independently might be significant SNP-SNP interactions associated with breast cancer Why is this Important? Medical: Determining SNP associations with Breast Cancer would: Help predict and prevent future cases Bioinformatics: Comparing two analysis techniques will: Help to create generalized method for analyzing future SNP interactions SNP-Single Nucleotide Polymorphism •A single nucleotide change at one particular locus •Must be present in at least 1% of the population •Can result in genotypic and phenotypic effects ACCGTTGTGACCTGCAGTGGAAACAGTATGA ACCATTGTGACATGCAGTGGAAACAGTGTGA www.dnalandmarks.com/.../marker_s ystems_snp.html Mechanisms of DNA Repair NER = nucleotide-excision repair, BER = base-excision repair, MMR = mismatch repair, DSBR =double strand break repair, DRCCD = damage recognition cell cycle delay response, NHEJ = non-homologous end-joining HR = Homologous Recombination DSBR pathway DSBR pathway Double stranded break repair pathway One mechanism responsible for the repair and maintenance of the integrity of DNA BRCA1 and 2 key elements in this pathway Vulnerability to breast cancer may be due to an individual’s capability in repairing damaged DNA Steps to Success Recreate data found in previous paper Implement Cordell and Clayton: Stepwise regression method Write up results and Create tables Future Direction: Compare results to Lasso method UCLA Cancer Registry UCLA familial cancer registry Participants may have cancer or not but must meet these criteria: Be 18 yrs or older Two family members with a same type of cancer or related cancers Or must have a family history of cancer susceptibility Mutation in BRCA1 or BRCA2 gene http://www.registry.mednet.ucla.edu/ Preliminary Work Case/control study 399 Caucasian (unrelated) women were chosen for study 104 SNPs in 17 genes of the DSBR pathway were chosen Logistic regression analysis conducted on each SNP to determine associations with breast cancer Adjusted models to include covariates Findings 12 significant SNPs Confirming Data: The Process First Step: Defining Variables Example of SNP rs16889040 on RAD21 gene, Chromosome 5 Additive Genotype. G–G A–G A– A Frequency 199 143 19 DV +0 +1 +2 Dominant DV +0 +1 +1 Additive • A allele confers risk in having breast cancer and A-A even more so Dominant • A allele confers risk in having breast cancer regardless of number of copies Example output from Logistic Regression Dominant Model rs16889040 Coefficients: Estimate (Intercept) -1.42388 age 0.04464 brca1 0.49067 brca2 -0.11683 EDUCATION1 0.08139 EDUCATION2 0.28671 Ashkenazi_status -0.68789 SNP -0.76382 Std. Error 0.72444 0.01305 0.39063 0.49631 0.33849 0.34757 0.28608 0.27855 z value -1.965 3.419 1.256 -0.235 0.240 0.825 -2.405 -2.742 Pr(>|z|) 0.049358 0.000628 0.209079 0.813896 0.809976 0.409424 0.016192 0.006104 Logit(Y) = B0 + B1X1 ….+ Bn Xn Education Double-Strand Break ATM Non-Homologous End Joining TP53 BRIP1 Homologous Recombination BRCA1 NBS1 ZNF350 RAD50 XRCC6 BRCA2 XRCC3 RAD51 MRE11A DNA-PK XRCC4 H2AX RAD54L LIG4 XRCC2 RAD52 H2AX RAD21 Repaired DNA XRCC5 Cordell and Clayton Method: Stepwise Logistic Regression Stepwise Logistic Regression: Stepwise logistic regression Cordell and Clayton Method used 8 genes that had significant SNPs in them Ran forward regression analysis on each gene Performed LRT and from test found p-value Cumulative Effects Cumulative Effects: SNPs in model but act independently Findings: No Accumulation of SNPS were found significant Interactive Effects Multiplicative effects- interaction between SNPs Findings: SNPd = rs16888927 SNPf = rs16888997 SNPg = rs16889040 RAD21 Gene interesting but not enough information to be considered significant SNPd: SNPf SNPd: SNPg SNPf: SNPg Three way interaction was found to be not significant SNP Interactions Using p-value threshold of 0.05 SNPs SNPd: SNPf SNPd: SNPg OR(eβ) 1.81212 1.76986 p-value 0.090404 0.096392 SNPf: SNPg 1.78383 0.090659 . Special Thanks To my amazing mentors at UCLA: Janet Sinsheimer PhD, Biostatistics lab Mary Sehl M.D., Dr. Sinsheimer’s lab UCLA For making the SoCalBSI program possible: The wonderful mentors at California State Los Angeles Dr. Momand , Dr. Warter Perez, Dr. Sharp, Dr. Johnston, Mr. Johnston, Dr. Huebach, Dr. Krilowicz Program Coordinator Ronnie Cheng Funding: American Society of Clinical Oncology – Mary Sehl National Science Foundation - SOCALBSI National Institute of Health - SOCALBSI Economic and Workplace Development -SOCALBSI Question Slides Recoding for Education Why Use Education? Why Only Caucasian Women? LRT/Chi^2 NEHJ and HR Multiple vs Independent LRT Test Three Way Interaction OR Lasso Method Recoding for Education Logistic Regression Education: 1-8 answers in a survey 1-3 highest education high school (control) 4-5 some college 6-8 higher education Educ1 Educ2 0 1 0 1-3 4-5 6-8 0 0 1 μ1 = μ + 0X α1 + 0Xα2 μ2 = μ + 1X α1 + 0X α2 μ3 = μ + 0X α1 + 1X α2 Coded in 0 and 1 transformation from linear to logistic Linear: Y = B0 + B1X1 ….+ Bn Xn Logistic: ln[ pi/(1-pin) ] = B0 + B1X1 ….+ Bn Xn Y == {0,1} Essentially the log of the probability of the odds Back Why Use Education as a Covariate? Routinely include at least 1 socioeconomic covariate Education: Not necessarily because statistically interesting, but because other studies have repeatedly found significance Back Why Only White Women? Homogeneous Population In different populations (men and other ethnicities), different genes may be involved Not enough sampling of any other group How data was found: Registry Website and Questionnaire in English Location of UCLA Etc… Back LRT Roughly estimated as a chi-squared distribution X2= 3.84 for 1 df P-val = .05 http://www.union.edu/PUBLIC/BIODEPT/chi.html Back Cell cycle with NEHJ and HR GC- use sister chromatid as template SSAhomologous sequences aligned, residues no longer present are deleted HR Alignment and ligation of termini at DSB http://www2.mrc-lmb.cam.ac.uk/personal/sl/Html/Graphics/CellCycle.gif Lord, Garret, Ashworth Clin Cancer Res 2006; 12(15) Back Multiple vs. Acting Independently Cumulative: Independent logit(P(Y)) = α + βTz +Ɣ1SNP1 + Ɣ2SNP2 Covariates Multiplicative: Combination of two logit(P(Y)) = α + βTz +Ɣ1SNP1 + Ɣ2SNP2 +Ɣ3SNP1*SNP2 Back LRT Test Testing for which model fits the data better For a 1 df, 3.84 or higher corresponds to a p-value of 0.05 or lower Alternative model fits the data better Equ: LRT= 2ln(L(HA)/L(H0) ) Less than 3.84 Null model fits the data better Back Three Way Interaction Covariates logit(P(Y)) = α + βTz +SNPd + SNPf + SNPg +SNPd*SNPf*SNPg Back ODDS RATIO Coded in 0 and 1 transformation from linear to logistic Linear: Y = B0 + B1X1 ….+ Bn Xn Logistic: ln[ pi/(1-pin) ] = B0 + B1X1 ….+ Bn Xn Y == {0,1} Odds Ratio is eB because of Logistic Regression’s Transformed form Back Lasso Penalized Regression Exploratory method used when large amount of predictors and small amount of data Penalizes model for having to many borderline significant predictors F(θ) = 1/2Σi(yi - μ –Σj(xijβj))2 + λΣj| βj | Least Squares Penalty Term Back