Research Report

Population Genetics May 12, 2015
Quantitative Risk Assessment for Breast Cancer in European American Women
By Amanda Jimcosky
Abstract
As a quantitative trait, breast cancer has many risk loci associated to it that currently
explain ~49% of the heritability of the disease (Fachal & Dunning, 2015). Through a
comprehensive list of SNPs collected from various published sources, partially known and
absolute risk scores can be assigned to theoretical individuals at highest, average, and lowest risk
as well as to any individual who has been genotyped through a service such as 23andme. The
calculated results show that my absolute risk of 34.58% is almost 3-fold higher than the average
European American, 12.68%. While my score is not particularly high-risk, such scores can
classify individuals’ risk and in turn could lead to preventative measures being more strictly
followed. The currently identified loci do not represent an individual’s true risk of developing
breast cancer because all risk loci have not been identified and the calculated risk scores do not
consider environmental, gene-environmental interactions, and non-additive genetic risk. With the
current knowledge, the high-risk classification that creates the largest divide between high and
low risk groups is approximately 12% of the population or the 88th percentile.
Introduction
Breast cancer is a quantitative trait that is impacted by many loci throughout the genome.
While genes such as BRCA1/2 have greater effect sizes (i.e. contribute more to overall risk),
many genes over the past ten years have been associated to the development of breast cancer.
While currently identified loci only represent approximately 49% of familial risk, the growing
knowledge of associated genes has led to a greater predictability of breast cancer development
than earlier predictions based primarily on high effect genes such as the BRCA genes (Fachal &

Dunning, 2015). In this study, I create a comprehensive list of loci that impact European
American women’s risk of developing breast cancer in order to calculate and compare an
individual’s risk to the average risk of the population and to assess whether I fall into a high or
low risk category for breast cancer.
Materials & Methods
The first step of this study was to compile a list of SNPs associated to breast cancer from
published sources, as well as the effect size or relative risk of each risk allele and the risk allele
frequency (Supplementary Table 1). Once the full list of SNPs associated with breast cancer was
complete, each was searched within my 23andme genotype data in order to determine if it was
available in the dataset and if so, to determine my genotype at the marker. The list of SNPs was
then narrowed down to those whose risk allele frequency, effect size, and genotype were all
available (Table 1). The most common genotype was determined assuming Hardy-Weinberg
equilibrium and using the risk allele frequency of each locus in order to create a comparison of
an individual with the overall most common genotype.
The statistical methods used in this study were based on the analysis completed by Sieh
et al. (2014) as follows. The partially known risk score was calculated first, using each variant’s
effect size, β, and number of risk alleles (0, 1, or 2), g:
s = b1g1 +...+bngn
The effect size β can be determined by taking the log of the relative risk of the effect allele. It
was found that many studies equated relative risk and odds ratio. Despite the general inaccuracy
of using these terms interchangeably, the reported values for either were used to determine the
effect size, following the trend seen in other literature. From the partially known risk score, the
absolute risk score was calculated using the following equation:

R =1-exp(-ces
),
where c is a positive constant determined by using the average absolute risk of the population,
12.68% , and the partially known risk score of an individual with the most common genotype, as
described above. Through these methods, the above equation for absolute risk was solved for c,
and c was calculated to be 0.0428 in this instance. The absolute risk score of the worst-case
scenario, an individual with two risk alleles at each locus, was calculated using this c value in
order to verify that the calculation was correct. Along with the risk scores for an individual with
the most common genotype and for an individual with highest risk, the risk scores for an
individual with no risk alleles was also calculated as a reference to my own risk scores.
Results
From nine different sources, 158 SNPs were reported as being linked to breast cancer
(Supplementary Table 1). However, five of these SNPs (rs1550623, rs1045485, rs11242675,
rs2380205, and rs12422552) were found insignificant by Michailidou et al. (2015), so they were
eliminated from the dataset. After narrowing the dataset to include only SNPs for whom the
effect size, risk allele frequency, and genotype data were available in the literature, 72 loci
remained for the analysis (Table 1).
Table 1. Summary of SNPs with all information (effect size, risk allele frequency, and genotype)
available. The calculated risk scores use only the information from the following loci.
Study Locus/SNP Gene Alleles1 RAF2 Relative Risk
Sieh et al. 1 rs11249433 NOTCH2/FCGR1B A/G 0.42 1.14
Sieh et al. 2 rs616488 PEX14 A/G 0.34 1.06
Sieh et al. 3 rs4849887 C/T 0.1 1.1
Sieh et al. 4 rs13387042 IGFBP2, IGBP5, TPN2 A/G 0.49 1.12
Sieh et al. 5 rs4973768 SLC4A7/NEK10 C/T 0.47 1.11
Sieh et al. 6 rs6762644 ITPR1/EGOT A/G 0.39 1.07
Sieh et al. 7 rs10941679 MRPS30/HCN1 A/G 0.25 1.19
Sieh et al. 8 rs889312 MAP3K1/MEIR3 A/C 0.28 1.13
Sieh et al. 9 rs1353747 PDE4D T/G 0.1 1.09

Sieh et al. 10 rs1432679 EBF1 T/C 0.43 1.07
Sieh et al. 11 rs204247 RANBP9 A/G 0.44 1.05
Sieh et al. 12 rs17530068 T/C 0.18 1.09
Sieh et al. 13 rs2046210 ESR1 G/A 0.35 1.13
Sieh et al. 15 rs720475 ARHGEF5/NOBOX G/A 0.24 1.06
Sieh et al. 16 rs9693444 C/A 0.32 1.07
Sieh et al. 17 rs13281615 MYC A/G 0.41 1.08
Sieh et al. 18 rs1011970 CDKN2A/B G/T 0.17 1.09
Sieh et al. 19 rs7072776 MLLT10/DNAJC1 G/A 0.28 1.07
Sieh et al. 20 rs10995190 ZNF365 G/A 0.15 1.16
Sieh et al. 21 rs704010 ZMIZ1 C/T 0.39 1.07
Sieh et al. 22 rs7904519 TCF7L2 A/G 0.45 1.06
Sieh et al. 23 rs11199914 C/T 0.32 1.05
Sieh et al. 24 rs2981579 FGFR2 G/A 0.42 1.26
Sieh et al. 25 rs3817198 LSP1/H19 T/C 0.32 1.07
Sieh et al. 26 rs10771399 PTHLH A/G 0.11 1.19
Sieh et al. 27 rs17356907 NTN4 A/G 0.3 1.1
Sieh et al. 28 rs1292011 TBX3/MAPKAP5 A/G 0.42 1.1
Fachal & Dunning 29 rs11571833 BRCA2 A/T 0.004 1.26
Sieh et al. 30 rs999737 RAD51B C/T 0.23 1.09
Sieh et al. 31 rs3803662 TOX3/LOC643714 G/A 0.28 1.2
Sieh et al. 32 rs17817449 MIR1972-2-FTO T/G 0.4 1.08
Sieh et al. 33 rs13329835 CDYL2 A/G 0.22 1.08
Sieh et al. 34 rs6504950 STXBP4/COX11 G/A 0.28 1.05
Sieh et al. 35 rs1436904 CHST9 T/G 0.41 1.04
Sieh et al. 36 rs8170 MERIT40 G/A 0.19 1.25
Sieh et al. 37 rs3760982 KCNN4/ZNF283 G/A 0.47 1.06
Sieh et al. 38 rs2284378 RALY C/T 0.204 1.16
Sieh et al. 39 rs2823093 NRIP1 G/A 0.26 1.09
Michailidou et al 40 rs2012709 C/T 0.46 1.06
Michailidou et al 41 rs10069690 TERT C/T 0.26 1.13
Michailidou et al 42 rs2363956 ANKLE1 G/T 0.49 1.19
Pharoah et al. 43 rs1053485 CASP8 C/A 0.86 1.13
Pharoah et al. 44 rs2981582 FGFR2 G/A 0.4 1.26
Fachal & Dunning 45 rs1562430 CASC21, CASC8 T/C 0.32 1.17
Fachal & Dunning 46 rs909116 TNNT3 T/C 0.53 1.17
Fachal & Dunning 47 rs9383938 ESR1 G/T 0.154 1.18
Fachal & Dunning 48 rs9485372 TAB2 G/A 0.241 1.11
Fachal & Dunning 49 rs13393577 ERBB4 T/C 0.113 1.53
Fachal & Dunning 50 rs4951011 ZC3H11A A/G 0.195 1.09
Fachal & Dunning 51 rs6964587 AKAP9 G/T 0.372 1.05
Bogdanova et al. 52 rs34767364 NBN/NBS1 G/A 0.001 1.9

Using the effect size of each SNP’s risk allele and g=2, the partial and absolute risk
scores for an individual with two copies of each risk allele were calculated to be 9.754 and 1.00,
respectively. Likewise, the partial and absolute risk scores of an individual with no copies of any
of the risk alleles were calculated, using g=0, to be 0.00 and 0.0419, respectively. The partially
known risk score of an individual with the average lifetime risk of 12.68% and the most common
genotype at each locus was calculated to be 1.154. Lastly, my personal genotype data from
23andme was used to calculate my partial and absolute risk scores, which are 2.295 and 0.3458,
respectively. This information is summarized in Table 2.
Peng et al. 53 rs1219648 FGFR2 A/G 0.42 1.32
Peng et al. 54 rs2180341 RNF146 A/G 0.21 1.41
Peng et al. 55 rs16886165 MAP3K1 G/T 0.15 1.23
Peng et al. 56 rs981782 A/C 0.53 1.04
Rennert et al. 57 rs36053993 MUTYH C/T 0.002 1.86
Li et al. 58 rs12443621 TOX3 A/G 0.572 1.01
Li et al. 59 rs8051542 TOX3 C/T 0.181 1.12
Johnson et al. 60 rs1799950 BRCA1 T/C 0.054 1.72
Johnson et al. 61 rs4986850 BRCA1 C/A 0.074 1.07
Johnson et al. 62 rs16942 BRCA1 A/G 0.322 1.46
Johnson et al. 64 rs766173 BRCA2 T/G 0.031 1.16
Johnson et al. 66 rs4987117 BRCA2 C/T 0.035 1.09
Johnson et al. 68 rs11571747 BRCA2 A/C 0.003 1.04
Johnson et al. 69 rs1800056 ATM T/C 0.011 1.52
Johnson et al. 70 rs1800058 ATM C/T 0.018 1.23
Johnson et al. 71 rs1801673 ATM A/T 0.005 1.41
Johnson et al. 72 rs1042522 TP53 C/G 0.262 1.02
1Reference/risk allele
2Risk allele frequency
[Note: Alleles, allele frequencies, and effect sizes were not always from the original source of the
identified SNP as listed in the left-hand column of the chart. These values were taken from the journal
articles listed on the reference page as well as refSNP, an NCBI SNP database.]

Table 2: Summary of Risk Assessment Results. The best-case, most common case, and
worst-case scenarios act as controls or comparisons for an individual’s risk scores. My data
yields an absolute risk score nearly three times higher than the average American woman.
Partially Known Risk Score Absolute Risk Score Lifetime Risk
Best-Case 0.00 0.0419 4.19%
Most Common 1.154 0.1268 12.68%
Worst-Case 9.754 1.0000 100.00%
Myself 2.295 0.3458 34.58%
Discussion
These calculated risk scores
can be used to categorize risk as either
high or low within the population
based on the average risk. Sieh et al.
(2014) evaluated various thresholds for
classifying someone as “high-risk”
(Figure 1). Roberts et al. (2012)
defined the high-risk category to
include individuals who exceeded the
90-95th percentile of risk. However,
the data reported by Sieh et al. (2014)
reveal that the greatest risk difference
between the high and low risk groups
occurs when the threshold for high-risk is set at approximately 12%, as indicated by the vertical
line on graph. This would set the cutoff at the 88th percentile, slightly lower than indicated by
Roberts et al (2012).
0
2
4
6
8
10
12
14
16
0 10 20 30 40 50 60 70 80 90 100
LifetimeRisk(%)
Percent of Population At High Risk
Low-Risk
High Risk
Figure 1: Comparison of risk groups for various thresholds of the high-risk
category. This data,taken from Sieh et al. (2014) shows that the greatest
difference in risk as currently understood occurs at a 12% cutoff, as indicated by
the vertical line. A lower cutoff increases the risk in both categories while a
higher cutoff decreases the risk in both categories. For optimal results,
individuals in the 88th percentile and higher should be classified as high-risk.

The calculations done in this study can relate to this designation of high and low risk
because a woman classified as high risk would benefit the most from implementing lifestyle
changes and medical interventions in order to avoid breast cancer or detect the disease early on.
In my case, my absolute risk is almost 3-fold higher than the average European American
woman. However, a risk of 34.58% is much lower than the typical classification of high-risk.
This information can guide me toward making healthier lifestyle choices, but it is also not strong
enough to, for example, encourage a medical provider or insurance company to recommend
rigorous preventative steps such as mastectomy or even early mammography. Understanding the
genetic underlying of breast cancer has the potential to aid individuals and doctors toward
personalized medicine based on overall risk. However, the full effectiveness of such testing
cannot be understood yet, as only approximately half of the heritability of breast cancer is
currently understood (Fachal & Dunning, 2015).
Another barrier to the interpretation of risk calculations is the exclusion of non-genetic
factors. It is well known that many environmental elements increase a person’s risk for various
forms of cancer, including breast cancer. However, in the analysis completed in this study,
environmental factors, gene-environment interaction, and non-additive genetic factors were
ignored. In the case that genetic testing was to be incorporated into routine medical care and the
risk of various diseases were to be evaluated, a more holist analysis would need to be developed
that would take all risk factors into consideration.

References
1. Bogdanova, N., Feshchenko, S., Schürmann, P., Waltes, R., Wieland, B., Hillemanns, P.,
… & Dörk, T. (2008). Nijmegen breakage syndrome mutations and risk of breast cancer.
International Journal of Cancer, 122, 802-806.
2. Fachal, L. & Dunning, A.M. (2015). From candidate gene studies to GWAS and post-
GWAS analyses in breast cancer. Current Opinion in Genetics and Development, 30, 32-
41.
3. Johnson, N., Fletcher, O., Palles, C., Rudd, M., Webb, E., Sellick, G., … & Peto, J.
(2007). Counting potentially functional variants in BRCA1, BRCA2, and ATM predicts
breast cancer susceptibility. Human Molecular Genetics, 16(9), 1051-1057.
4. Li, H., Beeghly-Fadiel, A., Wen, W., Lu, W., Gao, Y.T., Xiang, Y.B. … & Zheng, W.
(2013). Gene-environment interactions for breast cancer risk among Chinese women: a
report from the Shanghai breast cancer genetics study. American Journal of
Epidemiology, 177, 161-170.
5. Michailidou, K., Beesley, J., Lindstrom, S., Canisius, S., Dennis, J., Lush, M., … &
Easton, D. (2015). Genome-wide association analysis of more than 120,000 individuals
identifies 15 new susceptibility loci for breast cancer. Nature Genetics, 47(4), 373-380.
Retrieved April 9, 2015, from PubMed.
6. Michailidou, K., Hall, P., Gonzalez-Neira, A., Ghoussaini, M., Dennis, J., Milne, R.L., …
& Easton, D. (2013). Large-scale genotyping identifies 41 new loci associated with breast
cancer risk. Nature Genetics, 45, 353-361.
7. Pellatt, A.J., Wolff, R.K., Torres-Mejia, G., John, E.M., Herrick, J.S., Lundgreen, A., …
& Slattery, M.L. (2013). Telomere length, telomere-related genes, and breast cancer risk:
the breast cancer health disparities study. Genes Chromosomes Cancer, 52(7), 595-609.
Retrieved May 9, 2015, from PubMed.
8. Peng, S., Lu, B., Ruan, W., Zhu, Y., Sheng, H., & Lai, M. (2011). Genetic
polymorphisms and breast cancer risk: evidence from meta-analyses, pooled analyses,
and genome-wide association studies. Breast Cancer Research and Treatment, 127, 309-
324.
9. Pharoah, P., Antoniou, A., Bobrow, M., Zimmern, R., Easton, D., & Ponder, B. (2002).
Polygenic susceptibility to breast cancer and implications for prevention. Nature
Genetics, 31(1), 33-36. Retrieved April 9, 2015, from PubMed.
10. Pharoah, P., Antoniou, A., Easton, D., & Ponder, B. (2008). Polygenes, risk prediction,
and targeted prevention of breast cancer. The New England Journal of Medicine, 358(26),
2796-2803. Retrieved April 9, 2015, from PubMed.
11. Reference SNP (refSNP) Cluster Report. (n.d.) NCBI. Retrieved May 3, 2015.
12. Rennert, G., Lejbkowicz, F., Cohen, I., Pinchev, M., Rennert, H.S. & Barnett-Griness, O.
(2012). MUTYH mutation carriers have increased breast cancer risk. Cancer, 118, 1989-
1993.
13. Sieh, W., Rothstein, J., McGuire, V., & Whittemore, A. (2015). The role of genome
sequencing in personalized breast cancer prevention. Cancer Epidemiology, Biomarkers
& Prevention, 23(11), 2322-2327. Retrieved April 9, 2015, from PubMed.

Supplementary Material
Supplementary Table 1. Summary of SNPs associated with breast cancer risk. (Those highlighted in red were
found insignificant by Michailidou et al.)
Study Locus/SNP Gene Alleles1 RAF2 Relative Risk
Sieh et al. 1 rs11249433 NOTCH2/FCGR1B A/G 0.42 1.14
Sieh et al. 2 mult MUTYH - - 1.4-2.2
Sieh et al. 3 rs616488 PEX14 A/G 0.34 1.06
Sieh et al. 4 rs11552449 TPN22/BCL2L15 C/T 0.17 1.07
Sieh et al. 5 rs4245739 MDM4 A/C 0.27 1.14
Sieh et al. 6 rs4849887 C/T 0.1 1.1
Sieh et al. 7 mult MSH6 - - 4.9
Sieh et al. 8 mult MSH2 - - 2.4
Sieh et al. 9 rs12710696 C/T 0.36 1.11
Sieh et al. 10 rs2016394 METAP1D G/A 0.47 1.05
Sieh et al. 11 rs1550623 CDCA7 A/G 0.16 1.06
Sieh et al. 12 rs1045485 CASP8 G/C 0.13 1.03
Sieh et al.
13
rs13387042
IGFBP2, IGBP5,
TPN2 A/G 0.49 1.12
Sieh et al. 14 rs16857609 DIRC3 C/T 0.26 1.08
Sieh et al. 15 rs4973768 SLC4A7/NEK10 C/T 0.47 1.11
Sieh et al. 16 rs12493607 TGFBR2 G/C 0.35 1.06
Sieh et al. 17 rs6762644 ITPR1/EGOT A/G 0.39 1.07
Sieh et al. 18 rs9790517 TET2 C/T 0.22 1.05
Sieh et al. 19 rs6828523 ADAM29 C/A 0.12 1.11
Sieh et al. 20 rs10941679 MRPS30/HCN1 A/G 0.25 1.19
Sieh et al. 21 rs7734992 TERT C/T 0.43 1.05
Sieh et al. 22 rs889312 MAP3K1/MEIR3 A/C 0.28 1.13
Sieh et al. 23 rs10472076 RAB3C T/C 0.36 1.05
Sieh et al. 24 rs1353747 PDE4D T/G 0.1 1.09
Sieh et al. 25 rs1432679 EBF1 T/C 0.43 1.07
Sieh et al. 26 rs204247 RANBP9 A/G 0.44 1.05
Sieh et al. 27 rs17530068 T/C 0.18 1.09
Sieh et al. 30 rs11242675 FOXQ1 T/C 0.38 1.06
Sieh et al. 31 rs720475 ARHGEF5/NOBOX G/A 0.24 1.06
Sieh et al. 32 mult NBN - - 1.3-3.1
Sieh et al. 33 rs9693444 C/A 0.32 1.07
Sieh et al. 34 rs6472903 T/G 0.17 1.1
Sieh et al. 35 rs2943559 NGF4G A/G 0.07 1.13
Sieh et al. 36 rs13281615 MYC A/G 0.41 1.08

Sieh et al. 37 rs11780156 MIR1208 C/T 0.17 1.07
Sieh et al. 38 rs1011970 CDKN2A/B G/T 0.17 1.09
Sieh et al. 39 rs865686 KLF4/RAD23B T/G 0.37 1.12
Sieh et al. 40 rs10759243 C/A 0.27 1.06
Sieh et al. 41 rs2380205 ANKRD16 C/T 0.44 1.02
Sieh et al. 42 rs7072776 MLLT10/DNAJC1 G/A 0.28 1.07
Sieh et al. 43 rs11814448 DNAJC1 A/C 0.02 1.26
Sieh et al. 44 rs10995190 ZNF365 G/A 0.15 1.16
Sieh et al. 45 rs704010 ZMIZ1 C/T 0.39 1.07
Sieh et al. 46 mult PTEN - - 2.0-10.0
Sieh et al. 47 rs7904519 TCF7L2 A/G 0.45 1.06
Sieh et al. 48 rs11199914 C/T 0.32 1.05
Sieh et al. 49 rs2981579 FGFR2 G/A 0.42 1.26
Sieh et al. 50 rs3817198 LSP1/H19 T/C 0.32 1.07
Sieh et al. 51 rs3903072 OVOL1 G/T 0.47 1.05
Sieh et al. 52 rs614367 CCND1/FGFs C/T - 1.15
Sieh et al. 53 rs494406 CCND1 C/T 0.27 1.07
Sieh et al. 54 mult ATM - - 2.0-3.0
Sieh et al. 55 rs11820646 C/T 0.41 1.09
Sieh et al. 56 rs10771399 PTHLH A/G 0.11 1.19
Sieh et al. 57 rs12422552 G/C 0.26 1.05
Sieh et al. 58 rs17356907 NTN4 A/G 0.3 1.1
Sieh et al. 59 rs1292011 TBX3/MAPKAP5 A/G 0.42 1.1
Sieh et al. 60 mult BRCA2 - - 9.0-21.0
Fachal & Dunning 61 rs11571833 BRCA2 A/T .004 1.26
Sieh et al. 62 rs2236007 PAX9/SLC25A21 G/A 0.2 1.08
Sieh et al. 63 rs999737 RAD51B C/T 0.23 1.09
Sieh et al. 64 rs2588809 RAD51L1 C/T 0.15 1.08
Sieh et al. 65 rs941764 CCDC88C A/G 0.33 1.06
Sieh et al. 66 rs3803662 TOX3/LOC643714 G/A 0.28 1.2
Sieh et al. 67 rs17817449 MIR1972-2-FTO T/G 0.4 1.08
Sieh et al. 68 rs11075995 FTO T/A 0.23 1.1
Sieh et al. 69 mult CDH1 - - 2.0-10.0
Sieh et al. 70 mult PALB2 - - 2.0-4.0
Sieh et al. 71 rs13329835 CDYL2 A/G 0.22 1.08
Sieh et al. 72 mult BRCA1 - - 5.0-45.0
Sieh et al. 73 mult BRIP2 - - 2.0-3.0
Sieh et al. 74 mult TP53 - - 2.0-10.0
Sieh et al. 75 rs6504950 STXBP4/COX11 G/A 0.28 1.05
Sieh et al. 76 mult RAD51C - - 3.2-3.5
Sieh et al. 77 rs527616 G/C 0.37 1.05
Sieh et al. 78 rs1436904 CHST9 T/G 0.41 1.04
Sieh et al. 79 mult STK11 - - 2.0-10.0

Sieh et al. 80 rs8170 MERIT40 G/A 0.19 1.25
Sieh et al. 81 rs4808801 SSBP4/ISYNA1/ELL A/G 0.34 1.06
Sieh et al. 82 rs3760982 KCNN4/ZNF283 G/A 0.47 1.06
Sieh et al. 83 rs2284378 RALY C/T 0.204 1.16
Sieh et al. 84 rs2823093 NRIP1 G/A 0.26 1.09
Sieh et al. 85 mult CHEK2 - - 2.0-3.0
Michailidou et al 86 rs17879961 CHEK2 A/G 0.03 -
Sieh et al. 87 rs132390 EMID1/RHBDD3 T/C 0.03 1.12
Sieh et al. 88 rs6001930 MKL1 T/C 0.1 1.12
Michailidou et al 89 rs12405132 C/T - -
Michailidou et al 90 rs12048493 A/C 0.34 1.04
Michailidou et al 91 rs72755295 A/G 0.03 1.19
Michailidou et al 92 rs6796502 G/A 0.09 1.09
Michailidou et al 93 rs13162653 G/T 0.45 1.09
Michailidou et al 95 rs7707921 A/T 0.23 1.06
Michailidou et al 96 rs9257408 G/C 0.38 1.05
Michailidou et al 99 rs13267382 G/A 0.36 1.07
Michailidou et al 100 rs11627032 T/C 0.26 1.06
Michailidou et al 103 rs6678914 LGR6 G/A 0.42 -
Michailidou et al 104 rs1053338 ATXN7 A/G 0.13 1.07
Michailidou et al 105 rs10069690 TERT C/T 0.26 1.13
Michailidou et al 106 rs2736108 TERT C/T 0.27
Michailidou et al 107 rs17529111 FAM46A T/C 0.21
Michailidou et al 108 rs12662670 ESR1 T/G 0.08
Michailidou et al 109 rs78540526 CCND1? C/T 0.08
Michailidou et al 110 rs554219 CCND1? C/G 0.13
Michailidou et al 111 rs75915166 CCND1? G/A 0.06
Michailidou et al 112 rs2363956 ANKLE1 G/T 0.49 1.19
Pharoah et al. 113 rs1053485 CASP8 C/A 0.86 1.13
Pharoah et al. 114 rs2981582 FGFR2 G/A 0.40 1.26
Fachal & Dunning 115 rs1562430 CASC21, CASC8 T/C 0.32 1.17
Fachal & Dunning 116 rs909116 TNNT3 T/C 0.53 1.17
Fachal & Dunning 117 rs9383938 ESR1 G/T 0.154 1.18
Fachal & Dunning 118 rs10822013 ZNF365 C/T 0.421 1.12
Fachal & Dunning 119 rs9485372 TAB2 G/A 0.241 1.11
Fachal & Dunning 120 rs13393577 ERBB4 T/C 0.113 1.53
Fachal & Dunning 121 rs2290854 MDM4 G/A 0.461
Fachal & Dunning 122 rs4951011 ZC3H11A A/G 0.195 1.09

Fachal & Dunning 123 rs10474352 C/T 0.343 1.09
Fachal & Dunning 124 rs2290203 PRC1 G/A 0.375 1.08
Fachal & Dunning 125 rs6964587 AKAP9 G/T 0.372 1.05
Bogdanova et al. 126 rs34767364 NBN/NBS1 G/A 0.001 1.90
Peng et al. 127 rs1219648 FGFR2 A/G 0.42 1.32
Peng et al. 128 rs2180341 RNF146 A/G 0.21 1.41
Peng et al. 129 rs4784227 TOX3 C/T 0.20 1.24
Peng et al. 130 rs16886165 MAP3K1 G/T 0.15 1.23
Peng et al. 131 rs981782 A/C 0.53 1.04
Rennert et al. 132 rs24612342 MUTYH T/C - 1.39
Rennert et al. 133 rs36053993 MUTYH C/T 0.002 1.86
Li et al. 134 rs12443621 TOX3 A/G 0.572 1.01
Li et al. 135 rs8051542 TOX3 C/T 0.181 1.12
Johnson et al. 136 rs1799950 BRCA1 T/C 0.054 1.72
Johnson et al. 137 rs4986850 BRCA1 C/A 0.074 1.07
Johnson et al. 138 rs2227945 BRCA1 A/G 0.0004 0
Johnson et al. 145 rs11571746 BRCA2 T/C 0.0002 0
Johnson et al. 146 rs11571747 BRCA2 A/C 0.003 1.04
Johnson et al. 147 rs4987047 BRCA2 A/T 0.0002 0
Johnson et al. 148 rs1801426 BRCA2 A/G 0.0008 0
Johnson et al. 149 rs3218707 ATM G/C 0.0004 0
Johnson et al. 150 rs4987945 ATM C/G 0.0002 5.21
Johnson et al. 152 rs3218695 ATM C/A 0.010
Johnson et al. 154 rs1800057 ATM C/G 0.024 1.68
Johnson et al. 155 rs3092856 ATM C/T 0.0006 0
Johnson et al. 156 rs1800058 ATM C/T 0.018 1.23
Johnson et al. 157 rs1801673 ATM A/T 0.005 1.41
Johnson et al. 158 rs1042522 TP53 C/G 0.262 1.02
1Reference/risk allele
2Risk allele frequency
[Note: Alleles, allele frequencies, and effect sizes were not always from the original source of the identified
SNP as listed in the left-hand column of the chart. These values were taken from the journal articles listed on
the reference page as well as refSNP, an NCBI SNP database.]

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