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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