XRCC1 and OGG1 Gene Polymorphisms and Breast Cancer: A Systematic Review of Literature.

CONTEXT: Known polymorphisms of DNA repair genes can be associated with the risk of many types of cancer. There is no consensus regarding association between XRCC1 and OGG1 with breast cancer (BC).
OBJECTIVES: The aim of this study is to collect relevant published studies systematically. DATA SOURCES: Sixty-two publications were identified through searching PubMed, PubMed Central, ISI web of knowledge, and reference list of related articles. STUDY SELECTION: We performed a systematic review according MOOSE guideline criteria. All longitudinal cohort and case-control studies investigating association of any type and grade of breast cancer with XRCC1 and OGG1 gene and their polymorphisms were eligible for initial inclusion. DATA EXTRACTION: Two authors screened titles and abstracts and extracted all needed information from eligible studies. Four research methodological components causing bias for the association between gene polymorphisms and breast cancer risk, including source of controls sampling, population ethnicity, sample size of studies and menopausal status of cases and controls was used for assessment of quality of studies.
RESULTS: A total of 14,793 breast cancer cases and 15,409 controls were included in assessment of XRCC1 Arg194Trp. Four studies showed significant association and one study showed protective effect of XRCC1 Arg194Trp and BC. A total of 7,716 cases and 7,370 controls were included for XRCC1 Arg280His. Only one study showed significant association of XRCC1 Arg280His and breast cancer (OR = 1.82 (1.06 - 3.15). A total of 27,167 cases and 31,998 controls were included to estimate association between XRCC1 Arg399Gln polymorphism and breast cancer. Seven studies showed significant association and one showed protective effect of XRCC1 Arg399Gln and BC. A total of 9,417 cases and 11,087 controls were included for OGG1 Ser326Cys. Among studies focused on OGG1 Ser326Cys, none showed significant association with breast cancer.
CONCLUSIONS: Systematic search of major databases identify many studies addressing the relationship between BC and susceptible alleles in the base excision repair genes and the fact that there are many variations in the magnitude of association depending on inheritance model and the population of the study.

1. Context

Breast cancer (BC) counts the most common cause of cancer related death among women. The studies reported about 235303 new cases of BC and about 40430 death records per year in the United States (1). BC is the result of collaboration of many variables including environmental, reproductive, lifestyle, and genetic related factors (2) and approximately 10% of BC cases are attributed to genetic factors (3).

Mutations in high-penetrance genes such as BRCA1, BRCA2, TP53 and PTEN are demonstrated to contribute in BC susceptibility (4). However, they are counted only for 25% of inherited BC risk (5). A combined polymorphism effect of moderate and common low risk genes can clear remaining hereditary BC risk (6). Base-excision repair (BER) genes are an important subcategory of DNA repair genes, which fix DNA base damage due to X-rays, oxygen radicals, and alkylating agents (7). BER pathway excise oxidized DNA bases when oxidized DNA damage occur (8). Mutations in BER genes by cumulative effect of endogenous and exogenous mutagens result in apoptosis or cell overgrowth and cancer. Known polymorphisms of DNA repair genes can be associated with the risk of many types of cancer such as lung (9), colorectal (10), bladder (11), and BC (12). On the other hand, there are evidences demonstrating no association or protective effect of BER genes with breast cancer (11, 13, 14).

X-ray repair cross-complementing group 1 gene (XRCC1) and 8-oxoguanine DNA glycosylase (OGG1) play an important role in base-excision repair pathway. XRCC1 acts in BER encoding scaffolding protein that assemble together proteins of the DNA repair complex (15, 16). Arg194Trp, Arg280His and Arg399Gln amino acid substitution are the most common studied single nucleotide polymorphisms of XRCC1. OGG1 encodes a DNA glycosylase, an important enzyme in the repair of 8-oxoguanine (17). Amino acid replacement of serine (Ser) with cysteine (Cys) at codon 326 (Ser326Cys, rs1052133) as a result of common C/G single nucleotide polymorphism (SNP) in exon 7 of hOGG1 gene is associated with decreased hOGG1 repair activity (18).

There are studies recommending variation in association of genetic polymorphisms and diseases in different ethnicities and geographical populations (19, 20). There is no consensus among researchers regarding association between XRCC1 (21-24) and OGG1 (12, 25, 26) with BC. A case-control study in the United States and Poland populations which evaluated association of BER genes with BC (27) showed no association for XRCC1 and OGG1 in both populations. While other studies (28) (29) suggested association of XRCC1 (codon 194, codon 280, and codon 399) and OGG1 (codon 326) with breast cancer.

2. Objectives

As such, we aimed to conduct a comprehensive systematic review to address the association of XRCC1 and OGG1 polymorphisms with breast cancer.

3. Data Sources

The systematic review was conducted according to the meta-analysis of observational studies in epidemiology (MOOSE) group’s criteria (30). To extract all studies around genetic polymorphisms and breast cancer, we performed a sensitive search in PubMed, PubMed central and ISI web of knowledge from their commencements until September 2014. The search was carried out using keywords “Breast Neoplasms”, “Breast Tumor”, “Human Mammary Neoplasm”, “Human Mammary Carcinoma”, “Cancer of Breast, Breast Cancer”, “Mammary Cancer”, “Malignant Neoplasm of Breast”, “Malignant Tumor of Breast”, “Breast Carcinoma”, “Cancer of the Breast”, “Ductal Carcinoma of Breast”, “Lobular Carcinoma of Breast”, “Medullary Breast Cancer”, “Breast Tumor” and “In Situ Breast Cancer” with Boolean “OR” between them combining “Polymorphism” with Boolean “AND” (each dataset detailed search string and retrieved results reported in Appendix 1). Search results were limited to human studies. We also performed contemporary hand search of reference lists of final retrieved studies, meta-analysis and systematic reviews.

4. Study Selection

All longitudinal cohorts (conventional cohort and historical cohort studies) and case-control studies (conventional case-control, case-cohort, nested case-control, matched case-control and unmatched case-control studies) investigating association of breast cancer with XRCC1 and OGG1 genes and their polymorphisms were eligible for initial inclusion. Also, all types (carcinomas, sarcomas) and grades (low, intermediate/moderate and high grade) of breast cancer were considered in this review. The criteria for exclusion of articles on the basis of title and abstract screening were: 1) Studies with no control group, 2) non-research articles (all type of letters, comments, and editorial), 3) animal studies, 4) case reports and case series studies, 5) considered occurrence of secondary/metastatic breast cancer or all-cause mortality as an outcome , 6) considered patients undergoing any type of intervention and 7) studies without breast cancer (pre/post-menopausal) as outcome. If the same population had been studied in two or more different studies (articles), we chose the most comprehensive and the most recent one.

5. Data Extraction

Two authors screened titles and abstracts and extracted all needed information from eligible studies after exclusion of duplicate titles. If there was a disagreement in each stage, three authors discussed conflicting results. EndNote X7 software was used to manage review and organize screening.

The following data were extracted from each study: name of first authors, date of publication, study design, source of controls, considered confounders in each models, genotyping methods, population ethnicity, total number of cases and controls, menopausal status of cases and controls, number of cases and controls according to genotypes and menopausal status, mean age of cases and controls, frequency of each genotypes separately and odds ratios for homozygote, heterozygote and combined model.

Because of the potential hazards of assessing the quality of studies using mechanical checklist and scoring the pre-considered item which is not made specifically for genetic epidemiology researches (31-34). For quality assessment procedure, we used four research methodological components causing bias for the association between gene polymorphisms and breast cancer risk, including source of controls sampling, population ethnicity, sample size of studies and menopausal status of cases and controls (Appendix 2 to 5). In order to minimize potential errors, considered data extracted on a former piloted Microsoft Excel worksheet, accompanied by predefined instructions for reviewers. In final stage, senior researcher re-checked extracted data. If clarifications and more information (or unavailable full texts) were needed, we contacted the first and corresponding author for additional data.

6. Results

6.1. Eligible Studies

A total of 2424 titles were identified in initial electronic search. After final screening, 45 eligible publications (including 49 studies) through primary search, 13 publications (14 studies) through hand search of references list of retrieved citations and 6 through an update search of databases were retrieved (Figure 1). Two studies (35, 36) were excluded because of having overlapping population with two others (37, 38). Overall, 62 publications (67 studies) were included in this study according to inclusion and exclusion criteria (12, 21-23, 25-29)(37-89). General characteristics of all eligible studies separated for each gene are provided in Appendix 6 to 9. Different ethnicities populations in the same study were considered as separated study populations (five publications comprised of two populations with different ethnicities) (27, 43, 47, 68, 83). From these, 35 studies worked on Arg194Trp, 12 studies evaluated Arg280His, 56 assessed Arg399Gln, and 19 studies focused on Ser326Cys. All studies consist of retrospective confirmed breast cancer cases and cancer-free controls and were published during 2001 to 2015. Among retrieved studies, 17 also presented information on menopausal status and six studies were conducted on postmenopausal women. Considering geographical location, 24 studies were carried out in North America, 21 in Europe, 19 in Asia, 2 in Latin America and 2 in Africa. To have a better view of measure of association, ORs were recalculated for five studies which did not take wild type as reference category (46, 52, 56, 74, 87), four studies because of stratification were based on family history of breast cancer (22, 44, 48, 86) and one because of stratification was based on recreational activity (63).

Figure 1.

Flow Chart of the Reviewing Process for the Relationship Between Polymorphism and Breast Cancer

6.2. Arg194Trp

General characteristics of eligible studies are exhibited in Appendix 6. The 35 studies on Arg194Trp consisted of 14,793 breast cancer cases and 15,409 controls. Sample size of the studies ranged from 74 to 2,833 individuals. The largest sample size belonged to Zhang’s study (27) and Sterpone’s study had the lowest sample size (38). Tryptophan (Trp) allele frequency ranged from 0.03 to 0.53 in cases and 0.02 to 0.57 in controls. Most of the studies (16 of 35) were conducted in the United States of America. Clinic (or hospital) based case-control studies were the most common design (19 of 35). In addition, polymerase chain reaction-restriction fragment length polymorphism method (PCR- RFLP) was the most used genotyping method (20 of 35).

Among these 35 researches, four studies (28, 39, 70, 71)28, 39, 70, 71) showed significant association between Arg194Trp polymorphism and breast cancer comparing Trp carriers versus wild type and two (21, 59) showed marginally significant association. On the contrary, one study displayed protective effects (66). ORs ranged from not calculable (NC) (not enough number of exposed individuals) to 8.74 (adjusted) and NC to 3.87 (unadjusted) for homozygote versus wild type, from 0.44 to 4.42 (adjusted) and 0.35 to 4.59 (unadjusted) for heterozygote versus wild type and from 0.62 to 4.28 (adjusted) and 0.35 to 4.571 (unadjusted) for Trp carriers versus wild type. After considering stratification for menopausal status, only one study (28) reported significant association for post menopause and none showed association for premenopausal women. Oppositely, Patel (69) showed protective effect of Arg194Trp polymorphism on postmenopausal women (Table 1).

Table 1.

Adjusted and Unadjusted Odds Ratio of Studies Assessing the Association Between XRCC1 Arg194Trp Polymorphism and Breast Cancer

References	Menopausal Status	Trp/Trp Versus Arg/Arg[a]		Arg/Trp Versus Arg/Arg[a]		Trp/Trp and Arg/Trp Versus Arg/Arg[a]
		Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)
Duell E. J. (2001) (African-Americans) (47)	Mix	-	NC[b]	-	0.64 (0.28 - 1.42)[b]	0.70 (0.30 - 1.50)	-
Duell E. J. (2001) (White) (47)	Mix	-	NC[b]	-	0.75 (0.40 - 1.39)[b]	0.70 (0.40 - 1.30)	-
Kim S. U. (2002) (57)
	Mix	0.90 (0.48 - 1.67)	-	1.10 (0.76 - 1.73)	-	-	1.08 (0.71 - 1.62)[b]
	Pre	1.30 (0.57 - 2.95)	-	1.30 (0.74 - 2.18)	-	-	1.27 (0.74 - 2.18)[b]
	Post	0.50 (0.17 - 1.32)	-	1.00 (0.50 - 1.86)	-	-	0.83 (0.42 - 1.62)[b]
Han J. (2003) (51)	Mix	-	1.33 (0.17 - 10.00)[b]	-	0.79 (0.61 - 1.03)[b]	0.79 (0.60 - 1.04)	0.81 (0.62 - 1.05)
Moullan N. (2003) (67)	Mix	1.61 (0.10 - 26.10)	-	0.95 (0.56 - 1.61)	-	-	1.02 (0.61 - 1.71)[b]
Smith T. R. (2003) (84)	Mix	NC	-	1.47 (0.80 - 2.70)	-	1.60 (0.89 - 2.87)	-
Smith T. R. (2003) (22)	Mix	-	NC[b]	-	1.10 (0.58 - 2.02)[b]	-	1.12 (0.60 - 2.03)[b]
Deligezer U. (2004) (45)	Mix	-	NC	-	0.41 (0.16 - 1.06)	-	0.47 (0.19 - 1.17)
Forsti A. (2004) (50)	Mix	-	NC	-	1.27 (0.58 - 2.78)	-	-
Chacko P. (2005) (28)
	Mix	2.78 (0.82 - 9.40)	2.73 (0.81 - 9.20)	1.89 (0.99 - 3.62)	1.85 (1.01 - 3.38)	2.04 (1.12 - 3.72)	1.98 (1.13 - 3.48)
	Pre	-	-	-	-	1.35 (0.55 - 3.25)	-
	Post	-	-	-	-	2.75 (1.28 - 5.90)	-
Patel A. V. (2005) (69)	Post	-	-	-	-	0.62 (0.40 - 0.95)	0.66 (0.45 - 0.97)
Shen J. (2005) (81)
	Mix	-	-	-	-	0.93 (0.72 - 1.21)	-
	Pre	-	-	-	-	0.78 (0.50 - 1.20)	-
	Post	-	-	-	-	1.00 (0.72 - 1.38)	-
Brewster A. M. (2006) (41)	Mix	-	NC[b]	-	1.69 (0.99 - 2.87)[b]	1.20 (0.72 - 2.00)	1.12 (0.69 - 1.83)
Pachkowski B. F. (African-Americans) (2006) (68)	Mix	0.70 (0.10 - 4.40)	-	1.00 (0.70 - 1.30)	-	1.00 (0.70 - 1.30)	-
Pachkowski B. F. (Whites) (2006) (68)	Mix	0.90 (0.30 - 2.80)	-	0.90 (0.70 - 1.20)	-	0.90 (0.70 - 1.20)	-
Thyagarajan B. (2006) (23)	Mix	0.98 (0.21 - 4.63)	1.02 (0.23 - 4.57)	1.23 (0.21 - 4.63)	1.20 (0.77 - 1.87)	1.21 (0.78 - 1.88)	1.18 (0.77 - 1.81)
Zhang Y. (2006) (27)
	Mix	0.50 (0.20 - 1.40)	-	0.90 (0.80 - 1.20)	-	-	0.92 (0.73 - 1.15)[b]
	Pre	0.60 (0.00 - 10.30)	-	1.20 (0.80 - 1.80)	-	-	1.19 (0.81 - 1.77)[b]
	Post	0.50 (0.20 - 1.40)	-	0.80 (0.60 - 1.00)	-	-	0.75 (0.55 - 1.02)[b]
Silva S. N. (2007) (21)
	Mix	1.65 (0.10 - 27.43)	2.00 (0.12 - 32.14)	1.48 (0.95 - 2.32)	1.46 (0.94 - 2.26)	1.49 (0.95 - 2.31)	1.47 (0.95 - 2.27)
	Pre	NC	0.05 (0.00 - 4.04)	0.78 (0.16 - 3.68)	0.83 (0.18 - 3.69)	0.78 (0.16 - 3.68)	0.81 (0.18 - 3.63)
	Post	-	-	-	-	-	-
Loizidou M. A. (2008) (60)	Mix	-	0.95 (0.36 - 2.46)	-	1.02 (0.82 - 1.28)	-	1.02 (0.81 - 1.28)[b]
Mitra A. K. (2008) (66)	Mix	-	0.41 (0.08 - 2.08)	-	0.35 (0.18 - 0.65)	-	0.35 (0.19 - 0.64)
Sangrajrang S. (2008) (29)
	Mix	0.70 (0.42 - 1.14)	0.75 (0.47 - 1.20)	1.08 (0.81 - 1.44)	1.08 (0.82 - 1.41)		1.01 ( 0.77 - 1.32)[b]
	Pre	0.88 (0.45 - 1.73)	-	1.23 (0.83 - 1.82)	-	-	1.15 (0.80 - 1.65)[b]
	Post	0.55 (0.26 - 1.16)	-	0.92 (0.60 - 1.42)	-	-	0.85 (0.56 - 1.28)[b]
Smith T. R. (Caucasian) (2008) (83)	Mix	8.74 (0.97 - 78.23)	-	1.21 (0.74 - 1.97)	-	-	1.34 (0.82 - 2.18)[b]
Smith T. R. (African-American) (2008) (83)	Mix	NC	-	0.44 (0.12 - 1.67)	-	-	0.82 (0.23 - 2.73)[b]
Sobczuk A. (2009) (37)	Post	-	0.71 (0.33 - 1.54)[b]	-	0.74 (0.36 - 1.50)[b]	-	0.73 (0.37 - 1.41)[b]
Ming-Shiean H. (2010) (65)	Mix	1.37 (0.87 - 2.17)	1.36 (0.88 - 2.14)	1.12 (0.84 - 1.47)	1.09 (0.83 - 1.44)	1.20 (0.87 - 1.62)	1.17 (0.89 - 1.53)[b]
Sterpone S. (2010) (38)	Mix	-	NC	-	1.82 (0.43 - 7.66)	-	-
Zipprich J. (2010) (89)	Mix	-	-	-	-	1.17 (0.61 - 2.25)	-
Liu L. (2011) (59)	Mix	1.16 (0.84 - 1.59)	-	1.19 (0.99 - 1.44)	-	1.19 (0.99 - 1.41)	-
Al Mutairi F. M. (2013) (39)	Mix	-	NC	-	4.57 (1.47 - 14.21)	-	4.57 (1.47 - 14.21)
Przybylowska-Sygut K. (2013) (70)	Mix	-	NC	-	1.91 (1.15 - 3.14)	-	1.94 (1.15 - 3.32)[b]
Ding P. (2014) (46)	Mix	-	0.99 (0.66 - 1.48)[b]	-	0.93 (0.73 - 1.18)[b]	0.94 (0.75 - 1.18)	0.94 (0.74 - 1.18)[b]
McCullough L. E. (2014) (63)	Post	-	-	-	-	-	0.91 (0.62 - 1.32)[b]
Ramadan R. A. (2014) (71)	Mix	3.76 (1.07 - 13.17)	3.87 (1.24 - 12.05)	4.42 (2.08 - 9.41)	4.59 (2.33 - 9.06)	4.28 (2.11 - 8.70)	4.45 (2.35 - 8.45)
Smolarz B. (2014) (85)	Mix	-	0.89 (0.34 - 2.28)	-	0.59 (0.26 - 1.35)	-	0.68 (0.29 - 1.57)[b]
Macias-Gomez, N. M. (2015) (62)	Mix	-	1.41 (0.64 - 3.14)	-	1.01 (0.71 - 1.44)	-	1.06 (0.76 - 1.48)

Abbreviations: CI, confidence interval; NC, not calculable; OR, odds ratio.

aReferences category for calculation of odds ratio.

bORs calculated by authors via Stata software (version 13).

6.3. Arg280His

A total of 7,716 cases and 7,370 controls were studied for Arg280His. Zhang’s study (27) had the largest sample size (n = 2,802) and Smith’s study (83) (this study consists of two different ethnic populations and here the African American population was studied) had the lowest (n = 128). Histidine (His) allele had the least frequency among studies population comparing to other three alleles. His allele frequency ranged from 0.03 to 0.11 in cases and from 0.03 to 0.14 in controls. Six of studies (4 publications) were conducted in the United States of America (27, 68, 83, 89). Seven studies (six publications) had a clinic-based design and five (four publications) had a population-based design. Genotyping of four studies (28, 59, 60, 64) was carried out by PCR-RFLP (Appendix 7).

Results of Arg280His showed significant association between Arg280His polymorphism and breast cancer comparing His carriers versus wild type only in one study (67) with OR = 1.82 (1.06 - 3.15). ORs ranged from NC to 1.69 (adjusted) and NC to 4.68 (unadjusted) for homozygote versus wild type, from 0.55 to 1.80 (adjusted) and 0.56 to 1.13 (unadjusted) for heterozygote versus wild type and from 0.61 to 1.3 (adjusted) and 0.63 to 1.82 (unadjusted) for His carriers versus wild type. Stratification for menopausal status revealed marginally significant association in one study (29) (OR = 1.71 (0.94 - 3.10); P = 0.07) and protective association of Arg280His polymorphism in another study (28) (OR = 0.26 (0.1 - 0.66)) for post-menopausal women (Table 2).

Table 2.

Adjusted and Unadjusted Odds Ratio of Studies Assessing the Association Between XRCC1 Arg280His Polymorphism and Breast Cancer

References	Menopausal Status	His/His Versus Arg/Arg [a]		Arg/His Versus Arg/Arg [a]		His/His and Arg/His Versus Arg/Arg [a]
		Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)
Moullan N. (2003) (67)	Mix	NC	-	1.80 (1.04 - 3.08)	-	-	1.82 (1.06 - 3.15) [b]
Chacko P. (2005) (28)
	Mix	1.69 (0.29 - 9.63)	1.79 (0.32 - 10.05)	0.55 (0.29 - 1.03)	0.56 (0.29 - 1.05)	0.61 (0.33 - 1.12)	0.63 (0.35 - 1.15)
	Pre	-	-	-	-	1.44 (0.62 - 3.70)	-
	Post	-	-	-	-	0.26 (0.10 - 0.66)	-
Metsola K. (2005) (64)	Mix	-	NC [b]	-	1.13 (0.78 - 1.63) [b]	1.15 (0.80 - 1.66)	-
Pachkowski B. F. (African-Americans) (2006) (68)	Mix	1.10 (0.10 - 18.00)	-	1.30 (0.80 - 2.00)	-	1.30 (0.80 - 1.90)	-
Pachkowski B. F. (White) (2006) (68)	Mix	NC	-	1.20 (0.90 - 1.60)	-	1.20 (0.90 - 1.60)	-
Zhang Y. (2006) (27)
	Mix	1.00 (0.20 - 4.30)		1.10 (0.80 - 1.40)			1.10 (0.85 - 1.44) [b]
	Pre	0.30 (0.00 - 3.50)	-	1.00 (0.60 - 1.40)	-	-	0.94 (0.62 - 1.43) [b]
	Post	2.20 (0.20 - 21.20)	-	1.10 (0.80 - 1.60)	-	-	1.14 (0.80 - 1.63) [b]
Loizidou M. A. (2008) (60)	Mix	-	4.68 (1.01 - 21.70)	-	0.89 (0.71 - 1.11)	-	0.92 (0.73 - 1.15) [b]
Sangrajrang S. (2008) (29)
	Mix	-	-	-	-	1.30 (0.88 - 1.93)	1.09 (0.76 - 1.58)
	Pre	-	-	-	-	0.99 (0.58 - 1.70)	-
	Post	-	-	-	-	1.71 (0.94 - 3.10)	-
Smith T. R. (Caucasian) (2008) (83)	Mix	NC	-	0.72 (0.43 - 1.21)	-	-	0.70 (0.40 - 1.19)[b]
Smith T. R. (African-American) (2008) (83)	Mix	NC	-	0.66 (0.14 - 3.05)	-	-	-
Zipprich J. (2010) (89)	Mix	-	-	-	-	0.87 (0.38 - 2.00)	-
Liu L. (2011) (59)	Mix	0.50 (0.21 - 1.18)	-	1.02 (0.81 - 1.28)	-	0.98 (0.78 - 1.22)	-

Abbreviations: CI, confidence interval; NC, not calculable; OR, odds ratio.

aReferences category for calculation of odds ratio.

bORs calculated by authors via Stata software.

6.4. Arg399Gln

Arg399Gln was the most popularly evaluated polymorphism and 27167 cases and 31998 controls were studied to estimate association between Arg399Gln polymorphism and breast cancer. The sample sizes ranged from 74 to 7,25 individuals in which the largest and smallest studies were carried out by Sapkota (79) and Sterpone (38), respectively. Glutamine (Gln) allele frequency ranged from 0.14 to 0.55 in cases and 0.11 to 0.57 in controls. 19 of 56 studies (16 publications) were conducted in the United States of America. Thirty-one studies used PCR-RFLP method for genotyping and thirty-three studies had clinic based study design (Appendix 8).

Among studies analyzing Arg399Gln, seven (28, 38, 47, 61, 71, 80, 86) showed significant association between Arg399Gln polymorphism and breast cancer comparing Gln carriers versus wild type. Although six studies (27, 29, 53, 62, 64, 66) showed a borderline association, just one (64) had P < 0.05. Conversly, Zipprich et al. (89) revealed protective effect of Arg399Gln on breast cancer risk (OR = 0.64 (0.41 - 1.00)). Calculated ORs ranged from 0.44 to 4.42 (adjusted) and 0.25 to 4.40 (unadjusted) for homozygote versus wild type, from 0.65 to 2.64 (adjusted) and 0.65 to 4.8 (unadjusted) for heterozygote versus wild type and from 0.64 to 2.89 (adjusted) and 0.60 to 4.66 (unadjusted) for Gln carriers versus wild type. Arg399Gln polymorphism showed to have an association with breast cancer in one study for Premenopausal women (28) and three for postmenopausal woman (61, 72, 74). Nonetheless, one study showed protective effect of Arg399Gln polymorphism for breast cancer risk (57). Details of extracted ORs are reported in Table 3.

Table 3.

Adjusted and Unadjusted Odds Ratio of Studies Assessing the Association Between XRCC1 Arg399Gln Polymorphism and Breast Cancer

References	Menopausal Status	Gln/Gln Versus Arg/Arg[a]		Arg/Gln Versus Arg/Arg[a]		Gln/Gln and Arg/Gln Versus Arg/Arg[a]
		Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)
Duell E. J. (African-Americans) (2001) (47)	Mix	-	2.11 (0.52 - 9.99)[b]	-	1.54 (1.03 - 2.32)[b]	1.70 (1.10 - 2.40)	-
Duell E. J. (Whites) (2001) (47)	Mix	-	0.84 (0.52 - 1.33)[b]	-	1.12 (0.81 - 1.54)[b]	1.00 (0.80 - 1.40)	-
Kim S. U. (2002) (57)	Mix	2.40 (1.20 - 4.72)		0.80 (0.51–1.16)			0.96 (0.63 - 1.44)[b]
	Pre	3.80 (1.44 - 10.30)		1.20 (0.72–2.10)			1.51 (0.87 - 2.60)[b]
	Post	1.40 (0.50 - 3.91)		0.40 (0.19–0.74)			0.50 (0.25 - 0.98)[b]
Han J. (2003) (51)	Mix	1.03 (0.77 - 1.37)	1.06 (0.81 - 1.39)	1.07 (0.88–1.30)	1.06 (0.88 - 1.27)	-	1.04 (0.88 - 1.24)[b]
Moullan N. (2003) (67)	Mix	1.00 (0.57 - 1.76)	-	0.96 (0.66–1.40)	-	-	0.91 (0.64 - 1.29)[b]
Shu X. O. (2003) (82)	Mix	1.20 (0.85 - 1.69)	1.22 (0.87 - 1.71)	0.90 (0.76–1.08)	0.92 (0.77 - 1.09)		1.00 (0.84 - 1.18)[b]
	Pre	1.19 (0.78 - 1.80)	1.20 (0.79 - 1.81)	0.79 (0.63–0.98)	0.79 (0.63 - 0.98)		0.90 (0.73 - 1.10)[b]
	post	1.13 (0.61 - 2.11)	1.19 (0.65 - 2.17)	1.19 (0.87–1.62)	1.21 (0.90 - 1.64)		1.20 (0.90 - 1.62)[b]
Smith T. R. (2003) (84)	Mix	1.07 (0.58 - 1.96)	-	1.02 (0.70–1.51)	-	1.03 (0.71 - 1.49)	-
Smith T. R. (2003) (22)	Mix	-	1.09 (0.54 - 2.16)[b]	-	0.81 (0.53 - 1.25)[b]	-	0.86 (0.57 - 1.29)[b]
Deligezer U. (2004) (45)	Mix	-	1.26 (0.61 - 2.61)	-	0.88 (0.53 - 1.47)	-	0.96 (0.59 - 1.56)
Figueiredo J. C. (2004) (49)	Mix	0.88 (0.57 - 1.37)	0.92 (0.60 - 1.41)	0.91 (0.67-1.23)	0.92 (0.68 - 1.24)	-	0.92 (0.68 - 1.23)[b]
Forsti A. (2004) (50)	Mix	-	0.89 (0.46 - 1.72)	-	1.10 (0.75 - 1.61)	-	1.06 (0.73 - 1.52)[b]
Huang C. S. (2004) (52)	Mix		1.92 (0.79 - 4.69)[b]		0.99 (0.61 - 1.60)[b]		1.11 (0.71 - 1.75)[b]
Chacko P. (2005) (28)	Mix	2.69 (1.10 - 6.57)	2.66 (1.10 - 6.40)	2.04 (1.16-3.58)	2.02 (1.16 - 3.49)	2.18 (1.30 - 3.66)	2.14 (1.29 - 3.58)
	Pre					6.32 (2.90 - 13.73)
	Post					0.46 (0.36 - 1.57)
Dufloth R. M. (2005) (48)	Mix	-	0.89 (0.26 - 2.85)[b]	-	0.84 (0.45 - 1.58)[b]	-	0.85 (0.47 - 1.54)[b]
Metsola K. (2005) (64)	Mix	1.39 (0.84 - 2.29)	-	1.24 (0.93–1.65)	-	1.26 (0.96 - 1.66)	-
Patel A. V. (2005) (69)	Post	1.27 (0.79 - 2.02)	1.08 (0.71 - 1.63)	1.01 (0.73–1.39)	0.95 (0.71 - 1.27)	1.06 (0.78 - 1.44)	0.98 (0.75 - 1.28)
Shen J. (2005) (81)	Mix	0.97 (0.73 - 1.29)		1.08 (0.90-1.29)		1.06 (0.89 - 1.26)
	Pre	1.03 (0.62 - 1.70)		1.28 (0.93-1.75)		1.23 (0.91 - 1.66)
	Post	1.01 (0.71 - 1.44)		0.99 (0.79-1.24)		0.99 (0.80 - 1.24)
Brewster A. M. (2006) (41)	Mix	1.22 (0.73 - 2.06)	1.11 (0.67 - 1.83)	1.64 (1.00–2.69)	1.48 (0.92 - 2.38)	-	1.24 (0.88 - 1.75)[b]
Bu D. (2006) (42)	Mix	-	1.10 (0.48 - 2.53)	-	0.98 (0.58 - 1.65)	-	1.00 (0.58 - 1.69)[b]
Pachkowski B. F. (African-Americans) (2006) (68)	Mix	1.80 (0.80 - 3.80)	-	1.10 (0.90-1.50)	-	1.20 (0.80 - 1.50)	-
Pachkowski B. F. (Whites) (2006) (68)	Mix	1.00 (0.80 - 1.30)	-	1.10 (0.90-1.30)	-	1.10 (0.90 - 1.30)	-
Thyagarajan B. (2006) (23)	Mix	0.91 (0.48 - 1.72)	0.91 (0.49 - 1.70)	1.29 (0.84–1.97)	1.29 (0.85 - 1.95)	1.20 (0.80 - 1.79)	1.19 (0.80 - 1.77)
Zhai X. (2006) (88)	Mix	1.01 (0.60 - 1.70)		0.79 (0.57-1.08)		0.82 (0.61 - 1.11)
	Pre					0.83 (0.55 - 1.28)
	Post					0.82 (0.54 - 1.25)
Zhang Y. (USA) (2006) (27)	Mix	0.90 (0.80 - 1.10)		1.10 (0.90-1.20)			1.03 (0.92 - 1.15)[b]
	Pre	0.80 (0.60 - 1.10)		1.00 (0.80-1.20)			0.92 (0.77 - 1.11)[b]
	Post	1.10 (0.80 - 1.30)		1.10 (0.90-1.30)			1.09 (0.95 - 1.26)[b]
Zhang Y. (Poland) (2006) (27)	Mix	1.10 (0.90 - 1.40)		1.10 (1.00-1.30)			1.10 (0.97 - 1.25)[b]
	Pre	1.00 (0.70 - 1.50)		1.20 (0.90-1.50)			1.12 (0.88 - 1.44)[b]
	Post	1.10 (0.80 - 1.30)		1.10 (0.90-1.30)			1.08 (0.92 - 1.25)[b]
Costa S. (2007) (44)	Mix		0.95 (0.57 - 1.53)[b]		0.65 (0.47 - 0.90)[b]		1.64 (1.22 - 2.21)[b]
Silva S. N. (2007) (21)	Mix	0.87 (0.50 - 1.49)	0.80 (0.47 - 1.36)	0.87 (0.62–1.22)	0.83 (0.60 - 1.16)	0.87 (0.63 - 1.20)	0.83 (0.60 - 1.13)
	Pre	0.52 (0.14 - 2.61)	0.65 (0.14 - 3.04)	0.49 (0.17–1.42)	0.49 (0.17 - 1.35)	0.50 (0.18 - 1.32)	0.52 (0.20 - 1.32)
	Post
Ali M. F. (2008) (40)	Mix	-	3.54 (0.69 - 10.65)[b]	-	1.52 (0.51 - 4.59)[b]	-	2.05 (0.76 - 5.61)[b]
Kipikasova L. (2008) (58)	Mix	-	0.95 (0.39 - 2.27)[b]	-	1.25 (0.68 - 2.29)[b]	-	1.17 (0.67 - 2.04)[b]
Loizidou M. A. (2008) (60)	Mix	-	0.90 (0.68 - 1.18)	-	0.95 (0.80 - 1.14 )	-	0.94 (0.79 - 1.11)[b]
Mitra A. K. (2008) (66)	Mix	-	2.91 (1.66 - 5.10)	-	0.91 (0.56 - 1.50)	-	1.41 (0.90 - 2.19)
Saadat M. (2008) (77)	Mix	1.80 (0.90 - 3.61)	2.01 (1.02 - 3.94)	0.76 (0.47–1.20)	0.76 (0.49 - 1.18)	0.93 (0.61 - 1.44)	0.95 (0.63 - 1.42)
	Pre	1.93 (0.81 - 4.62)	1.87 (0.81 - 4.37)	0.88 (0.46–1.67)	0.84 (0.45 - 1.56)	1.09 (0.61 - 1.96)	1.06 (0.60 - 1.85)
	Post	1.20 (0.23 - 6.07)	1.03 (0.21 - 5.05)	0.48 (0.18–1.31)	0.46 (0.17 - 1.21)	0.57 (0.22 - 1.48)	0.53 (0.21 - 1.34)
Sangrajrang S. (2008) (29)	Mix	1.80 (0.99 - 3.29)	1.74 (0.99 - 3.10)	1.20 (0.90–1.60)	1.17 (0.89 - 1.53)		1.23 (0.94 - 1.61)[b]1.09
	Pre	1.45 (0.60 - 3.52)		1.05 (0.71–1.55)			(0.75 - 1.57)[b]
	Post	1.84 (0.79 - 4.28)		1.30 (0.84–2.00)			1.40 (0.93 - 2.11)[b]
Smith T. R. (Caucasian) (2008) (83)	Mix	0.93 (0.56 - 1.54)	-	1.01 (0.74-1.40)	-	1.00 (0.73 - 1.35)	1.03 (0.75 - 1.40)[b]
Smith T. R. (African-American) (2008) (83)	Mix	2.19 (0.09 - 52.25)	-	1.13 (0.44-2.91)	-	1.18 (0.47 - 2.96)	1.33 (0.53 - 3.29)[b]
Sobczuk A. (2009) (37)	Post	-	0.89 (0.42 - 1.84)[b]	-	1.09 (0.54 - 2.18)[b]	-	0.99 (0.52 - 1.88)[b]
Syamala V. S. (2009) (86)	Mix	-	1.58 (0.99-2.52)[b]	-	1.60 (1.15 - 2.23)[b]	-	1.59 (1.17 - 2.16)[b]
Jakubowska A. (2010) (54)	Mix	0.67 (0.36 - 1.25)	0.67 (0.40 - 1.12)	0.73 (0.48-1.10)	0.85 (0.60 - 1.19)		0.80 (0.57 - 1.13)[b]
Jelonek K. (2010) (55)	Mix	-	0.94 (0.43 - 1.93)[b]	-	0.72 (0.44 - 1.20)[b]	-	0.77 (0.48 - 1.23)[b]
Ming-Shiean H. (2010) (65)	Mix	1.31 (0.83 - 1.96)	1.26 (0.82 - 1.93)	1.05 (0.81–1.38)	1.03 (0.78 - 1.36)	1.12 (0.87 - 1.42)	1.07 (0.82-1.40)[b]
Romanowicz H. (2010) (74)	Post	-	2.79 (1.51 - 5.21)[b]	-	1.29 (0.83 - 1.99)[b]	-	1.59 (1.06 - 2.40)[b]
Santos R. A. (2010) (78)	Mix	-	0.25 (0.04 - 1.30)	-	0.73 (0.30 - 1.40)	-	0.60 (0.30 - 1.30)
Sterpone S. (2010) (38)	Mix	-	4.40 (1.13 - 17.08)	-	4.80 (1.56 - 14.78)	-	4.66 (1.47 - 15.25)[b]
Zipprich J. (2010) (89)	Mix	0.44 (0.18 - 1.11)	-	0.65 (0.39-1.10)	-	0.64 (0.41 - 1.00)	-
Liu L. (2001) (59)	Mix	0.91 (0.66 - 1.27)	-	0.87 (0.72–1.05)	-	0.88 (0.74 - 1.05)	-
Roberts M. R. (2011) (72)	Mix
	Pre	0.79 (0.44 - 1.42)		0.88 (0.62 – 1.24)		0.86 (0.62 - 1.20)
	Post	1.31 (0.94 - 1.83)		1.22 (0.99 – 1.51)		1.24 (1.01 - 1.51)
Hussien Y. M. (2012) (53)	Mix	1.60 (0.60 - 4.10)	-	1.70 (0.90 – 3.10)	-	1.70 (0.90 - 2.90)	-
Al Mutairi F. M. (2013) (39)	Mix	-	1.25 (0.46 - 3.39)	-	0.94 (0.51 - 1.72)	-	1.00 (0.57 - 1.75)
Przybylowska-Sygut K. (2013) (70)	Mix	-	0.86 (0.46 - 1.60)	-	1.16 (0.76-1.78)	-	1.08 (0.70 - 1.65)[b]
Sapkota Y. (2013) (79)	Mix	1.11 (0.94 - 1.31)	-	1.12 (1.00 - 1.25)	-	-	-
Ding P. (2014) (46)	Mix	-	2.69 (1.71 - 4.31)[b]	-	0.89 (0.70 - 1.14)[b]	1.10 (0.88 - 1.37)	1.09 (0.87 - 1.38)[b]
Luo H. (2014) (61)	Mix	2.18 (1.06 - 4.50)		1.52 (1.01 - 2.31)		1.67 (1.12 - 2.47)
	Pre	0.77 (0.20 - 2.91)		1.53 (0.66-3.51)		1.33 (0.60 - 2.91)
	Post	2.57 (0.96 - 6.89)		1.84 (1.08-3.15)		1.94 (1.16 - 3.24)
McCullough L. E. (2014) (63)	Post	-	-	-	-	-	1.11 (0.87 - 1.42)[b]
Ramadan R. A. (2014) (71)	Mix	4.42 (1.28 - 15.31)	3.56 (1.22 - 10.39)	2.64 (1.20 – 5.08)	2.62 (1.35 - 5.08)	2.89 (1.35 - 6.21)	2.77 (1.47 - 5.24)
Shadrina A. S. (2014) (80)	Mix	1.76 (1.21 - 2.57)	-	1.17 (0.92 – 1.51)	-	1.29 (1.02 - 1.63)	-
Macias-Gomez (2015)N. M. (62)	Mix	-	2.71 (1.44 - 5.10)	-	1.10 (0.80 - 1.51)	-	1.27 (0.94 - 1.72)

Abbreviations: CI, confidence interval; OR, odds ratio; USA, United States of America.

aReferences category for calculation of odds ratio.

bORs calculated by authors via Stata software (version 13).

6.5. Ser326Cys

A total of 9,417 cases and 11,087 controls were founded to be considered for Ser326Cys. Respectively, Roberts’ study (72) and Sterpone’s (38) study had the largest and smallest sample sizes of 2,941 and 74 individuals. Cysteine (Cys) allele was the most prevalent allele in this study and Cys frequency ranged from 0.21 to 0.63 in cases and 0.21 and 0.68 in controls. About half of studies of Ser326Cys polymorphism were conducted in Asia, particularly on Chinese populations. Design of 13 studies (12 publications) was clinic based (Appendix 9).

Among studies focusing on Ser326Cys, none showed significant association between Ser326Cys polymorphism and breast cancer comparing Cys carriers to wild type. Calculated ORs ranged from 0.69 to 1.70 (adjusted) and 0.22 to 1.46 (unadjusted) for homozygote versus wild type, from 0.82 to 1.20 (adjusted) and 0.33 to 1.37 (unadjusted) for heterozygote versus wild type and from 0.76 to 1.21 (adjusted) and 0.28 to 1.28 (unadjusted) for Cys carriers versus wild type. Stratification for menopausal status exhibited no significant association between Ser326Cys and breast cancer in pre and post-menopausal women, but Romanowicz-Makowska (75) showed protective effect in post-menopausal women (OR = 0.49 (0.24 - 0.98)) (Table 4).

Table 4.

Adjusted and Unadjusted Odds Ratio of Studies Assessing the Association Between OGG1 Ser326Cys Polymorphism and Breast Cancer

First Author	Menopausal Status	Cys/Cys Versus Ser/Ser[a]		Ser /Cys Versus Ser/Ser[a]		Cys/Cys and Ser /Cys Versus Ser/Ser[a]
		Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)	Unadjusted OR (95% CI)
Choi J. Y. (Korean) (2003) (43)
	Mix	1.20 (0.71 - 2.00)	-	0.90 (0.59 - 1.53)	-	-	0.94 (0.59 - 1.49)[b]
	Pre	1.10 (0.57 - 2.22)	-	1.00 (0.56 - 1.93)	-	-	1.03 (0.55 - 1.91)[b]
	Post	1.10 (0.46 - 2.45)	-	0.70 (0.34 - 1.63)	-	-	0.77 (0.37 - 1.64)[b]
Choi J. Y. (Japanese) (2003) (43)
	Mix	1.70 (0.93 - 2.96)	-	1.20 (0.76 - 1.95)	-	-	1.28 (0.81 - 2.02)[b]
	Pre	1.90 (0.75 - 4.90)	-	1.30 (0.63 - 2.69)	-	-	1.32 (0.65 - 2.66)[b]
	Post	1.30 (0.61 - 2.98)	-	1.20 (0.64 - 2.42)	-	-	1.23 (0.65 - 2.34)[b]
Vogel U. (2003) (26)	Post	-	0.98 (0.52 - 1.86)	-	0.84 (0.64 - 1.10)	-	0.85 (0.64 - 1.13)[b]
Huang C. S. (2004) (52)	Mix	-	0.82 (0.42 - 1.61)[b]	-	0.90 (0.48 - 1.71)[b]	-	0.86 (0.48 - 1.58)[b]
Cai Q. (2006) (25)
	Mix	1.06 (0.83 - 1.35)	-	1.17 (0.93 - 1.47)	-	-	1.10 (0.88 - 1.38)[b]
	Pre	0.99 (0.74 - 1.34)	-	1.08 (0.81 - 1.43)	-	-	1.04 (0.79 - 1.37)[b]
	Post	1.18 (0.77 - 1.81)	-	1.37 (0.91 - 2.07)	-	-	1.26 (0.84 - 1.88)[b]
Rossner P., Jr. (2006) (76)	Mix	1.01 (0.68 - 1.52)	0.99 (0.66 - 1.47)	1.06 (0.88 - 1.28)	1.04 (0.87 - 1.24)	-	1.02 (0.86 - 1.22)[b]
Zhang Y. (2006) (27)
	Mix	1.00 (0.70 - 1.40)	-	1.00 (0.80 - 1.20)	-	-	0.98 (0.83 - 1.14)[b]
	Pre	0.90 (0.50 - 1.50)	-	1.10 (0.80 - 1.40)	-	-	1.02 (0.78 - 1.32)[b]
	Post	1.20 (0.70 - 1.90)	-	1.00 (0.80 - 1.20)	-	-	1.01 (0.82 - 1.25)[b]
Romanowicz-Makowska H. (2008) (75)	Post	-	0.62 (0.28 - 1.38)[b]	-	0.40 (0.18 - 0.87)[b]	-	0.49 (0.24 - 0.98)[b]
Sangrajrang S. (2008) (29)
	Mix	1.42 (0.97 - 2.09)	1.46 (1.02 - 2.10)	0.95 (0.67 - 1.34)	0.99 (0.72 - 1.37)	-	1.14 (0.83 - 1.56)[b]
	Pre	1.13 (0.67 - 1.91)	-	0.92 (0.58 - 1.48)	-	-	1.12 (0.72 - 1.73)[b]
	Post	2.05 (1.14 - 3.69)	-	0.97 (0.58 - 1.61)	-	-	1.17 (0.72 - 1.89)[b]
Synowiec E. (2008) (87)	Mix	-	0.22 (0.04 - 1.04)[b]	-	0.33 (0.06 - 1.40)[b]	-	0.28 (0.05 - 1.10)[b]
Ming-Shiean (2010)H. (65)	Mix	1.09 (0.74 - 1.59)	1.09 (0.74 - 1.63)	0.97 (0.66 - 1.42)	0.99 (0.67 - 1.47)	1.04 (0.80 - 1.46)	1.02 (0.71 - 1.48)[b]
Sterpone S. (2010) (38)	Mix	-	0.83 (0.10 - 6.65)	-	1.37 (0.53 - 3.56)	-	1.03 (0.46 - 3.64)[b]
Roberts M. (2011)R. (72)
	Mix	-	-	-	-	-	-
	Pre	1.17 (0.53 - 2.54)	-	1.00 (0.72 - 1.38)	-	1.01 (0.74 - 1.38)	-
	Post	1.19 (0.77 - 1.85)	-	0.97 (0.79 - 1.19)	-	0.99 (0.82 - 1.21)	-
Kim K. Y. (2013) (57)	Mix	-	0.83 (0.53 - 1.30)[b]	-	0.95 (0.66 - 1.38)[b]	0.90 (0.64 - 1.27)	0.91 (0.64 - 1.30)[b]
Xie H. (2013) (12)
	Mix	-	1.34 (0.97 - 1.84)	-	1.10 (0.82 - 1.49)	-	1.19 (0.88 - 1.60)[b]
	Pre	-	1.87 (1.14 - 3.06)	-	1.08 (0.68 - 1.72)	-	1.34 (0.84 - 2.15)[b]
	Post	-	1.18 (0.73 - 1.89)	-	1.02 (0.66 - 1.57)	-	1.07 (0.69 - 1.65)[b]
Luo H. (2014) (61)
	Mix	0.69 (0.40 - 1.19)	-	0.82 (0.48 - 1.38)	-	0.76 (0.46 - 1.24)	-
	Pre	0.41 (0.14 - 1.18)	-	0.58 (0.21 - 1.64)	-	0.49 (0.19 - 1.27)	-
	Post	0.83 (0.40 - 1.70)	-	1.01 (0.51 - 1.99)	-	0.93 (0.49 - 1.76)	-
McCullough L. E. (2014) (63)	Post	-	-	-	-	-	0.98 (0.77 - 1.25)[b]
Rodrigues P. (2014) (73)	Mix	-	-	-	-	1.82 (1.31 - 2.52)[c]	-
Smolarz B. (2014) (85)	Mix	-	0.93 (0.34 - 2.51)	-	1.02 (0.45 - 2.34)	-	1.00 (0.42 - 2.37)[b]

Abbreviations: CI, confidence interval; OR, odds ratio.

aReferences category for calculation of odds ratio.

bORs calculated by authors via Stata software (version 13).

cCys/Cys versus Ser /Cys and Ser/Ser; Cys/Cys as reference category.

As part of a systematic review to address variations seen in the association between polymorphic genes involved in base excision repair pathway and risk of breast cancer, we identified all studies indexed in PubMed and Web of Sciences in which the risk of barest cancer for carriers of susceptibility alleles of XRCC1 and OGG1 genes were investigated. Collectively, there were 62 studies assessing the BER gene polymorphism in different population providing a wealth of information to look at the cause of variation in allele frequency as well as the magnitude of association between this polymorphic genes and breast cancer.

Etiology of breast cancer is characterized with major influence of endogenic estrogen exposure and gens involved in cancer susceptibility or genes involved in different pathways of estrogen metabolism (90). The lifestyle and environmental factors play important role in modifying the magnitude of estrogen exposure in a background of several susceptibility genes (91). The highly penetrate genes of BRCA1 and BRCA2 accounts for up to 25% of inherited breast cancer (where not more than 10% of breast cancer are considered as familial and inherited) (92, 93). The involvement of low-penetrate susceptibility polymorphic genes involved in the pathophysiologic pathways of breast tumor genesis play major role in sporadic breast cancer (94). The polymorphic genes can be involved in several cellular mechanisms especially estrogen metabolism pathways, detoxification of xenobiotic, cell adhesion, and DNA damage and repair signaling. While polymorphic genes involved in estrogen metabolism are very specific to breast cancer susceptibility, polymorphic genes involved in DNA repair and damage play an important role in the etiology of many cancers including breast (95). Among the many polymorphic genes involved in the etiology of breast cancer, Base excision repair polymorphic genes shows especially important role as a mechanism involved in repairing endogenous driven DNA damage, which may originate from wide range of normal metabolism. The magnitude of association (expressed as odds ratio in case-control and rate ration in cohort studies) between low penetrance genes and risk of cancer is very small and barely ranges more than 1.5. From population standpoint, the small magnitude of association seen between the susceptibility allele and risk of cancer is aggregated into an important causal factor in the light of high prevalence of susceptibility allele. As the systematic review result, the prevalence of susceptibility allele in controls group for XCCR1 Arg194Trp ranged from 2% in populations of Finland (50) and Saudi Arabia (39) to 57% in the population of Poland (37). For XRCC1 Arg280His the prevalence ranged between 3% in African-American population of USA (68) to 14% in population of India (28). For XRCC1 Arg399Gln, the prevalence ranged between 11% for African-American population of the US (83) to 57% for the population of Poland (37). For OGG1 Ser326Cys, the prevalence ranged between 21% for populations of Spain (73) and USA (27) to 68% for the population of Poland (87). The distribution of a susceptibility allele in a population is supposed to follow the Hardy-Weinberg law, unless a known population stratification or funder effect disturbs the HWE law (96). In studies of association between a susceptible allele and risk of cancer when there is no indication of population stratification or funder effect, the differential frequency of the susceptibility allele among the diseased compared to non-diseased provides the bases for association studies. Lack of HWE law in distribution of susceptible allele confounds any study assessing association between susceptibility allele and cancer risk (97, 98). In our systematic review, it was observed that HWE law was assessed for all the studies.

In association studies of susceptibility allele and risk of breast cancer, controlling other risk factors such as risk factors related to life style and hormonal play an important role. Many studies reviewed in this systematic review included control for known risk factors; in some studies, the analysis were done based on subgroup analysis and some studies used statistical modeling. Studies included in our analysis control extraneous risk factors either through subgroup analysis (grouping the study subjects into pre and post menopause) or by using multivariable models. Many of the studies were case-control studies some of which were population and clinical based studies. As population based case and controls are less prone to bias, our systematic review identifies many studies whose results can be used for further studies such as met-analysis.

In epidemiology, systematic reviews are done for different purposes. Our systematic review’s aim was to identify all studies that assessed the relationship between risk of breast cancer and susceptibility alleles of main base excision repair genes (XRCC1 and OGG1). The systematic approach makes sure that all studies are included and properly evaluated. The fact that our systematic review was able to use major databases (PubMed and Web of Sciences) indicates that we were able to identify all studies indexed in these two databases which cover a large part of English language indexed publications. The inclusion of two databases and manual search of the references of other systematic reviews and meta analyses have lower chances of missing major and important studies indicating high degrees of completeness.

The measure of association was another factor, which may be different among different populations. The difference depends on the frequency and magnitude of other modifying factors effecting and interacting with the BER genes. Our systematic review reveals that, the association between risk of breast cancer and XCRR1 and OGG1 susceptible allele depended on the model of inheritance and different model can show different effects on different population. Further studies are needed to assess the effect(s) of inheritance model on the risk of breast cancer.

7. Conclusions

Systematic search of major databases identify many studies addressing the relationship between breast cancer and susceptible alleles in the base excision repair genes and the fact that there are many variations in the magnitude of association depending on model of inheritance and the population of the study.

Appendix A1.

Details of Electronic Search Strategy [a]

Database	Search Strategy	N
PubMed/NLM	(“Polymorphism”) AND (“Breast Neoplasms” OR “Breast Tumor” OR “Human Mammary Neoplasm” OR “Human Mammary Carcinoma” OR “Cancer of Breast” OR “Breast cancer” OR “Mammary Cancer” OR “Malignant Neoplasm of Breast” OR “Malignant Tumor of Breast” OR “Breast Carcinoma” OR “Cancer of the Breast” OR “Ductal carcinoma of breast” OR “Lobular Carcinoma of breast” OR “Medullary breast cancer” OR “Breast tumor” OR “In Situ breast cancer”)	5345
PMC/NLM	(“Polymorphism”[ti]) AND (“Breast Neoplasms” [ti] OR “Breast Tumor” [ti] OR “Human Mammary Carcinoma” [ti] OR “Cancer of Breast”[ti] OR “Breast cancer”[ti] OR “Mammary Cancer”[ti] OR “Malignant Neoplasm of Breast”[ti] OR “Malignant Tumor of Breast”[ti] OR “Breast Carcinoma”[ti] OR “Cancer of the Breast”[ti] OR “Ductal carcinoma of breast”[ti] OR “Lobular Carcinoma of breast”[ti] OR “Medullary breast cancer”[ti] OR “Breast tumor”[ti] OR “In Situ breast cancer”[ti])	138
ISI Web of Science/Thomson Reuters	(Polymorphism [a] breast cancer) Refined by: LANGUAGES: ( ENGLISH ) AND Databases: ( WOS ) AND RESEARCH AREAS: ( ONCOLOGY OR GENETICS HEREDITY OR BIOCHEMISTRY MOLECULAR BIOLOGY OR GERIATRICS GERONTOLOGY ) AND DOCUMENT TYPES: ( ARTICLE OR REVIEW OR CORRECTION ) Timespan: All years.	1501

a Calculated OR for different strata, but data not shown.

Appendix A2.

Quality of Studies Assessing the Association Between Ser326Cys Polymorphism and Breast Cancer [a,b]

Study (year)	Source of Control	Population Ethnicity	Sample Size	Menopausal Status	HWE Reported
Choi (2003)	+	+	+	+	+
Vogel (2003)	+	-	+	+	+
Huang (2004)	+	-	+	-	-
Cai (2006)	+	+	+	+	+
Rossner (2006)	+	-	+	A	+
Zhang (2006)	+	+	+	+	+
Sangrajrang (2008)	+	-	+	+	+
Synowiec (2008)	-	-	+	-	+
Romanowicz-Makowska (2008)	-	-	+	+	+
Ming-Shiean (2010)	+	-	+	-	+
Sterpone (2010)	B	+	+	-	+
Roberts (2011)	+	A,C	+	+	+
Kim (2013)	+	-	+	-	+
Xie (2013)	+	+	+	+	+
Luo (2014)	+	+	+	+	+
McCullough (2014)	+	D	+	+	+
Rodrigues (2014)	+	+	+	-	+
Smolarz (2014)	+	-	+	-	+

a Definitions: Year, year of publication; Source of control means clinic (or hospital) based or population based; population ethnicity means Caucasian, African-American and other ethnicity; Sample size means calculable sample size; Menopausal status means adjustment of menopausal status (post, pre or both) for final measure of association using methods such as adjustment in regression models, restriction, stratification etc; HWE (Hardy-Weinberg equilibrium) means calculated or reported HWE equilibrium.

b A, Adjusted, but no detail presented; B, Source of controls: volunteers; C, Matched controls, but no detail presented; D, Only mentioned to ethnicity without adjustment.

Appendix A3.

Quality of Studies Assessing the Association Between XRCC1 Arg194Trp Polymorphism and Breast Cancer [a,b]

Study (year)	Source of Control	Population Ethnicity	Sample Size	Menopausal Status	HWE Reported
Duell (2001)	+	+	+	A	+
Kim (2002)	+	-	+	+	+
Han (2003)	+	B	+	C	+
Smith (2003)	+	+	+	-	+
Smith (2003)	+	+	+	-	+
Moullan (2003)	+	-	+	-	+
Forsti (2004)	+	-	+	-	+
Deligezer (2004)	-	-	+	-	+
Chacko (2005)	+	-	+	+	+
Shen (2005)	+	A	+	+	+
Patel (2005)	+	BD	+	+	+
Pachkowski (2006)	+	+	+	-	+
Thyagarajan (2006)	+	-	+	-	+
Brewster (2006)	+	+	+	C	+
Zhang (2006)	+	+	+	+	+
Silva (2007)	+	+	+	+	+
Smith (2008)	+	+	+	-	+
Sangrajrang (2008)	+	-	+	+	+
Mitra (2008)	E	+	+	D	+
Loizidou (2008)	E	+	+	-	+
Sobczuk (2009)	-	-	+	+	+
Zipprich (2010)	+	D	+	C	+
Ming-Shiean (2010)	+	-	+	-	+
Sterpone (2010)	E	+	+	-	+
Liu (2011)	+	+	+	-	+
Przybylowska-Sygut (2013)	+	-	+	-	+
Al Mutairi (2013)	+	D	+	-	+
Ding (2014)	+	+	+	-	+
McCullough (2014)	+	B	+	+	+
Ramadan (2014)	+	-	+	A	+
Smolarz (2014)	+	-	+	-	+
Macias-Gomez (2015)	-	-	+	-	+

a Definitions:Year, year of publication; positive sign (+), mentioned in the articles; negative sign (-), not mentioned in the article; Source of control means clinic (or hospital) based or population based; Population ethnicity means Caucasian, African-American and other ethnicity; sample size means calculable sample size; Menopausal status means adjustment of menopausal status (post, pre or both) for final measure of association using methods such as adjustment inregression models, restriction, stratification etc; HWE (Hardy Weinberg equilibrium) means calculated or reported HWE equilibrium.

b A, Calculated OR for different strata, but data not shown; B, Only mentioned to ethnicity without adjustment; C, Adjusted, but no detail presented; D, Matched controls, but no detail presented; E, Source of controls: volunteers.

Appendix A4.

Quality of Studies Assessing the Association Between Arg280His Polymorphism and Breast Cancer [a,b]

Study (year)	Source of Control	Population Ethnicity	Sample Size	Menopausal Status	HWE Reported
Moullan (2003)	+	-	+	-	+
Chacko (2005)	+	-	+	+	+
Metsola (2005)	+	+	+	A	+
Pachkowski (2006)	+	+	+	-	+
Zhang (2006)	+	+	+	+	+
Smith (2008)	+	+	+	-	+
Loizidou (2008)	B	+	+	-	+
Sangrajrang (2008)	+	-	+	+	+
Zipprich (2010)	+	C	+	D	+
Liu (2011)	+	+	+	-	+

a Definitions: Year, year of publication; Source of control means clinic (or hospital) based or population based; Population ethnicity means Caucasian, African-American and other ethnicity; Sample size means calculable sample size; Menopausal status means adjustment of menopausal status (post, pre or both) for final measure of association using methods such as adjustment in regression models, restriction, stratification etc; HWE (Hardy Weinberg equilibrium) means calculated or reported HWE equilibrium.

b A, Calculated OR for different strata, but data not shown; B, Source of controls: volunteers; C, Matched controls, but no detail presented; D, Adjusted, but no detail presented.

Appendix A5.

Quality of Studies Assessing the Association Between Arg399Gln Polymorphism and Breast Cancer [a,b]

Study (year)	Source of Control	Population Ethnicity	Sample Size	Menopausal Status	HWE Reported
Duell (2001)	+	+	+	A	+
Kim (2002)	+	-	+	+	+
Han (2003)	+	B	+	C	+
Shu (2003)	+	+	+	+	+
Smith (2003)	+	+	+	-	+
Smith (2003)	+	+	+	-	+
Moullan (2003)	+	-	+	-	+
Deligezer (2004)	-	-	+	-	+
Figueiredo (2004)	+	B,C	+	-	+
Forsti (2004)	+	-	+	-	+
Huang (2004)	+	-	+	-	-
Metsola (2005)	+	+	+	A	+
Chacko (2005)	+	-	+	+	+
Dufloth (2005)	D	-	+	-	-
Patel (2005)	+	BE	+	+	+
Shen (2005)	+	A	+	+	+
Brewster (2006)	+	+	+	C	+
Bu (2006)	+	B	+	-	+
Pachkowski (2006)	+	+	+	-	+
Thyagarajan (2006)	+	-	+	-	+
Zhai (2006)	+	+	+	+	+
Zhang (2006)	+	+	+	+	+
Costa (2007)	+	-	+	-	+
Silva (2007)	+	+	+	+	+
Ali (2008)	+	-	+	-	-
Kipikasova (2008)	D	+	+	-	+
Mitra (2008)	D	+	+	E	+
Loizidou (2008)	D	+	+	-	+
Saadat (2008)	-	+	+	+	+
Smith (2008)	+	+	+	-	+
Sangrajrang (2008)	+	-	+	+	+
Sobczuk (2009)	-	-	+	+	+
Syamala (2009)	+	-	+	-	-
Jelonek (2010)	+	+	+	-	+
Jakubowska (2010)	+	-	+	-	-
Ming-Shiean (2010)	+	-	+	-	+
Sterpone (2010)	D	+	+	-	+
Santos (2010)	D	-	+	-	+
Romanowicz (2010)	-	-	+	+	+
Zipprich (2010)	+	E	+	C	+
Liu (2011)	+	+	+	-	+
Roberts (2011)	+	C,E	+	+	+
Hussien (2012)	+	-	+	-	+
Al Mutairi (2013)	+	E	+	-	+
Sapkota (2013)	+	+	+	-	+
Przybylowska-Sygut (2013)	+	-	+	-	+
Ding (2014)	+	+	+	-	+
Luo (2014)	+	+	+	+	+
Ramadan	+	-	+	A	+
Shadrina (2014)	+	+	+	-	+
Macias-Gomez (2015)	-	-	+	-	+

a Year, year of publication; Source of control means clinic (or hospital) based or population based; Population ethnicity means Caucasian, African-American and other ethnicity; Sample size means calculable sample size; Menopausal status means adjustment of menopausal status (post, pre or both) for final measure of association using methods such as adjustment in regression models, restriction, stratification etc; HWE (Hardy Weinberg equilibrium) means calculated or reported HWE equilibrium.

b A, Calculated OR for different strata, but data not shown; B, Only mentioned to ethnicity without adjustment; C, Adjusted, but no detail presented; D, Source of controls: volunteers; E, Matched controls, but no detail presented.

Appendix A6.

Characteristics of Literature Included in the Systematic Review Evaluating the Association Between XRCC1 Arg194Trp Polymorphism and Breast Cancer [a]

First Author	Date	Country	Ethnicity	Study Design	Control Source	Genotyping Methods	Analyzed Sample Size (Case Number)	Minor Allele Frequency (Case/Control)	Considered Confounders [b]
Duell, E. J.	2001	USA	African-Americans	population based	population based	multiplex PCR-RFLP and 59-exoncuclease (Taqman)	327 (161)	(0.05/0.06)	age
Duell, E. J.	2001	USA	Whites	population based	population based	multiplex PCR-RFLP and 59-exoncuclease (Taqman)	485 (251)	(0.05/0.06)	age
Kim, S. U.	2002	Korea	NS	hospital based	hospital based	PCR-RFLP	410 (205)	(0.34/0.34)	breast feeding, family history of breast cancer and menopausal status at baseline
Han, J.	2003	USA	Caucasian, Southern European, Scandinavian, Asians, Hispanics, and African Americans	population based	population based	pyrosequencing, RFLP, and the 5 nuclease assay (TaqMan)	2367 (998)	(0.05/0.06)	age; menopausal status; postmenopausal hormone use; date of blood draw; time of blood draw; fasting status, body mass index at age 18 years, weight gain since age 18 years, age at menarche, age at menopause, parity/age at first birth, first-degree family history of breast cancer, history of benign breast disease, alcohol intake, during smoking, and duration of postmenopausal hormone use
Moullan, N.	2003	France	NS	hospital based	population based	TaqMan technology or variant specific restriction enzyme digestion	566 (254)	(0.07/0.06)	age
Smith, T. R.	2003	USA	Caucasian	hospital based	hospital based	PCR-RFLP	512 (246)	(0.07/0.04)	age, family history, age at menarche, age at first live birth, and body mass index
Smith, T. R.	2003	USA	Caucasian	hospital based	hospital based	PCR-RFLP	463 (162)	(0.07/0.06)	age
Deligezer, U.	2004	Turkey	NS	NS	NS	multiplex PCR	284 (151)	(0.03/0.05)	-
Forsti, A.	2004	Finland	NS	NS	population based	PCR-RFLP	521 (223)	(0.03/0.02)	-
Chacko, P.	2005	India	NS	hospital based	hospital based	PCR-RFLP and DNA sequencing techniques	246 (123)	(0.21/0.12)	age and menopausal status
Patel, A. V.	2005	USA	white, African-American, Hispanic, Asian, other/unknown	population based	population based	Taqman	970 (485)	NC	matched pairs, body mass index, age at menopause, age at menarche, alcohol intake, education, parity, age at first live birth, physical activity, history of breast cysts, family history of breast cancer in mother or sister, use of hormone replacement therapy, and smoking status
Shen, J.	2005	USA	White or non-White	population based	population based	fluorescence polarization method	2177 (1067)	NC	age at menarche, parity, lactation, months of lactation, age at first birth, number of miscarriages, history of fertility problems, body mass index at reference, body mass index at age 20, first-degree family history of breast cancer, history of benign breast disease, menopausal status, oral contraceptive use, hormone replacement use, race, education, religion, and marital status
Brewster, A. M.	2006	USA	Mostly Caucasian	population based	population based	patented fluorogenic method for nucleic acid analysis(Taqman or 5’nuclease assay) and PCR-RFLP	615 (305)	(0.06/0.06)	family history
Pachkowski, B. F.	2006	USA	African Americans	population based	Population based	the ABI 7700 sequence detection system or ‘‘Taqman’’ assay	1456 (774)	(0.07/0.07)	offsets and age
Pachkowski, B. F.	2006	USA	Whites	population based	population based	the ABI 7700 sequence detection system or ‘‘Taqman’’ assay	2407 (1281)	(0.06/0.07)	offsets and age
Thyagarajan, B.	2006	USA	NS	population based	population based	PCR-RFLP, PCR-INVADER, or Sequenom	626 (309)	(0.07/0.06)	age, family history of breast cancer among first degree relatives, physical activity, body mass index
Zhang, Y.	2006	USA	Caucasian	Population based	population based	TaqMan	2833 (1580)	(0.06/0.07)	age and study site
Silva, S. N.	2007	Portugal	Caucasian Portuguese	hospital based	hospital based	PCR-RFLP	693 (240)	(0.08/0.06)	age at diagnosis; alcohol consumption and smoking habits
Loizidou, M. A.	2008	Greece	Greek Cypriots	population based	volunteers	PCR-RFLP	2261 (1097)	( 0.08/0.08)	-
Mitra, A. K.	2008	India	Caucasians of Indo-European	hospital based	volunteers	PCR-RFLP	376 (151)	(0.06/0.14)	-
Sangrajrang, S.	2008	Thailand	NS	hospital based	hospital based	rapid capillary PCR followed by melting curve analysis using fluorescence labeled hybridization probes in a light cycler	931 (506)	(0.30/0.31)	age, body mass index, age at menarche, family history of breast cancer, reproduction parameters, use of contraceptives, menopausal status, hazardous lifestyle, and education
Smith, T. R.	2008	USA	Caucasian	hospital based	Hospital based	mass ARRAY system	735 (324)	(0.07/0.05)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Smith, T. R.	2008	USA	African-American	hospital based	hospital based	mass ARRAY system	128(53)	(0.06/0.06)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Sobczuk, A.	2009	Poland	NS	NS	NS	PCR-RFLP	256 (150)	(0.53/0.57)	-
Ming-Shiean, H.	2010	Taiwan	NS	hospital based	hospital based	real-time PCR with the TaqMan assay system	934 (401)	(0.32/0.28)	age, age at first full-term pregnancy, cigarette smoking, body mass index, and family history
Sterpone, S.	2010	Italy	Caucasian	hospital based	volunteers	PCR-RFLP	74 (43)	(0.08/0.04)	-
Zipprich, J.	2010	USA	NS	population based	population based	fluorescence polarization method	559 (254)	NC	menopause and age and age at menarche
Liu, L.	2011	China	Han Chinese	hospital based	population based	PCR-RFLP	1999 (995)	(0.30/0.27)	age
Al Mutairi, F. M.	2013	Saudi Arabia	NS	hospital based	hospital based	TaqMan SNP genotype analysis technique	200(100)	(0.08/0.02)	-
Przybylowska-Sygut, K.	2013	Poland	NS	hospital based	population based	PCR-RFLP	390 (185)	(0.14/0.08)	-
Ding, P.	2014	China	Han Chinese	hospital based	hospital based	polymerase chain reaction-ligase detection reaction (PCR-LDR)	1239 (606)	(0.32/0.32)	age
McCullough, L. E.	2014	USA	White and black	population based	population based	fluorescence polarization (FP) method	1166 (598)	NC	age
Ramadan, R. A.	2014	Egypt	NS	hospital based	hospital based	PCR-RFLP	175(100)	(0.40/0.20)	age, body mass index , parity, menopausal status, age at menarche, family history of breast cancer, smoking, or physical activity
Smolarz, B.	2014	Poland	NS	hospital based	hospital based	PCR-RFLP	140 (70)	(0.49/0.50)	-
Macias-Gomez, N. M.	2015	Mexico	NS	hospital based	NS	PCR-RFLP	697 (345)	(0.16/0.15)	-

a Definitions: Date, year of publication; NS, not state; NC, not calculable; USA, United State of America.

b Confounders in multivariate analysis.

Appendix A7.

Characteristics of Literature Included in the Systematic Review Evaluating the Association Between XRCC1 Arg280His Polymorphism and Breast Cancer [a]

First Author	Date	Country	Ethnicity	Study Design	Control Source	Genotyping Methods	Analyzed Sample Size (Case Number)	Minor Allele Frequency (Case/Control)	Considered Confounders [b]
Moullan, N.	2003	France	NS	hospital based	population based	TaqMan technology or variant specific restriction enzyme digestion	566 (254)	(0.08/0.04)	age
Chacko, P.	2005	India	NS	hospital based	hospital based	PCR-RFLP and DNA sequencing techniques	246 (123)	(0.11/0.14)	age and menopausal status
Metsola, K.	2005	Finland	Caucasian	hospital based	population based	PCR-RFLP	959 (480)	(0.08/0.07)	age, age at menarche, age at first full term pregnancy, number of pregnancies, history of benign breast disease, first degree family history of breast cancer, waist-to-hip ratio, smoking and use of alcohol
Pachkowski, B. F.	2006	USA	African Americans	population based	population based	the ABI 7700 sequence detection system or ‘‘Taqman’’ assay	1446 (765)	(0.04/0.03)	offsets and age
Pachkowski, B. F.	2006	USA	Whites	population based	population based	the ABI 7700 sequence detection system or ‘‘Taqman’’ assay	2400 (1271)	(0.05/0.04)	offsets and age
Zhang, Y.	2006	USA	Caucasian	population based	population based	TaqMan	2802 (1564)	(0.05/0.04)	age and study site
Loizidou, M. A.	2008	Greece	Greek Cypriots	population based	volunteers	PCR-RFLP	2277 (1109)	( 0.08/0.09)	-
Sangrajrang, S.	2008	Thailand	NS	hospital based	hospital based	rapid capillary PCR followed by melting curve analysis using fluorescence labeled hybridization probes in a light cycler	932 (507)	(0.08/0.07)	age, body mass index, age at menarche, family history of breast cancer, reproduction parameters, use of contraceptives, menopausal status, hazardous lifestyle, and education
Smith, T. R.	2008	USA	Caucasian	hospital based	hospital based	mass ARRAY system	732 (324)	(0.04/0.05)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Smith, T. R.	2008	USA	African-American	hospital based	hospital based	mass ARRAY system	128 (53)	(0.03/0.04)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Zipprich, J.	2010	USA	NS	population based	population based	fluorescence polarization method	599 (271)	NC	menopause and age and age at menarche
Liu, L.	2011	China	Han Chinese	hospital based	population based	PCR-RFLP	1999 (995)	(0.09/0.10)	age

a Definitions: Date, year of publication; NS, not state; NC, not calculable; USA, United State of America.

b Confounders in multivariate analysis.

Appendix A8.

Characteristics of Literature Included in the Systematic Review Evaluating the Association Between XRCC1 Arg399Gln Polymorphism and Breast Cancer [a]

First Author	Date	Country	Ethnicity	Study Design	Control Source	Genotyping Methods	Analyzed Sample Size (Case Number)	Minor Allele Frequency (Case/Control)	Considered Confounders [b]
Duell, E. J.	2001	USA	African-Americans	population based	population based	multiplex PCR-RFLP and 59-exoncuclease (Taqman)	519(253)	(0.19/0.14)	age
Duell, E. J.	2001	USA	Whites	population based	population based	multiplex PCR-RFLP and 59-exoncuclease (Taqman)	767(386)	(0.35/0.36)	AGE
Kim, S. U.	2002	Korea	NS	hospital based	hospital based	PCR-RFLP	410(205)	(0.35/0.31)	breast feeding, family history of breast cancer and menopausal status at baseline
Han, J.	2003	USA	Caucasian, Southern European, Scandinavian, Asians, Hispanics, and African Americans	population based	population based	pyrosequencing, RFLP, and the 5 nuclease assay (TaqMan)	2323(986)	(0.37/0.36)	age; menopausal status; postmenopausal hormone use; date of blood draw; time of blood draw; fasting status, body mass index at age 18 years, weight gain since age 18 years, age at menarche, age at menopause, parity/age at first birth, first-degree family history of breast cancer, history of benign breast disease, alcohol intake, during smoking, and duration of postmenopausal hormone use
Moullan, N.	2003	France	NS	hospital based	population based	TaqMan technology or variant specific restriction enzyme digestion	566(254)	(0.34/0.35)	age
Shu, X. O.	2003	China	Han Chinese	population based	population based	PCR-based RFLP	2270(1088)	(0.28/0.27)	age, education, family history of breast cancer, menopausal status, age at menarche, age at menopause, body mass index, waist: hip ratio, and physical activity
Smith, T. R.	2003	USA	Caucasian	hospital based	hospital based	PCR-RFLP	518(251)	(0.36/0.33)	age, family history, age at menarche, age at first live birth, and body mass index
Smith, T. R.	2003	USA	Caucasian	hospital based	hospital based	PCR-RFLP	462(162)	(0.34/0.35)	age
Deligezer, U.	2004	Turkey	NS	NS	NS	multiplex PCR	284(151)	(0.39/0.37)	-
Figueiredo, J. C.	2004	Canada	Caucasian-Native or Caucasian-Asian or Caucasian-Black	population based	population based	matrix-assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry	804(402)	(0.35/0.37)	ethnicity, age, and family history of breast cancer
Forsti, A.	2004	Finland	NS	NS	population based	PCR-RFLP	521(223)	(0.32/0.32)	-
Huang, C. S.	2004	Taiwan	NS	hospital based	hospital based	PCR-RFLP	368(136)	(0.27/0.23)	-
Chacko, P.	2005	India	NS	hospital based	hospital based	PCR-RFLP and DNA sequencing techniques	246 (123)	(0.34/0.21)	age and menopausal status
Dufloth, R. M.	2005	Brazil	NS	hospital based	volunteers (among hospital personnel)	PCR-RFLP	205(86)	(0.27/0.29)	-
Metsola, K.	2005	Finland	Caucasian	hospital based	population based	PCR-RFLP	957(479)	(0.30/0.27)	age, age at menarche, age at first full term pregnancy, number of pregnancies, history of benign breast disease, first degree family history of breast cancer, waist-to-hip ratio, smoking and use of alcohol.
Patel, A. V.	2005	USA	White, African-American, Hispanic, Asian, other/unknown	population based	population based	Taqman	904(452)	(0.35/0.34)	matched pairs, body mass index, age at menopause, age at menarche, alcohol intake, education, parity, age at first live birth, physical activity, history of breast cysts, family history of breast cancer in mother or sister, use of hormone replacement therapy, and smoking status
Shen, J.	2005	USA	White or non-White	population based	population based	fluorescence polarization method	2177(1067)	( 0.36/0.35)	age at menarche, parity, lactation, months of lactation, age at first birth, number of miscarriages, history of fertility problems, body mass index at reference, body mass index at age 20, first-degree family history of breast cancer, history of benign breast disease, menopausal status, oral contraceptive use, hormone replacement use, race, education, religion, and marital status
Brewster, A. M.	2006	USA	mostly Caucasian	population based	population based	patented fluorogenic method for nucleic acid analysis(Taqman or 5’nuclease assay) and PCR-RFLP	615(305)	(0.38/0.37)	family history
Bu, D.	2006	USA	Caucasian, Black, Hispanic, Ashkenazi Jewish	hospital based	hospital based	multiplex primer extension PCR	285(190)	(0.34/0.33)	-
Pachkowski, B. F.	2006	USA	African Americans	population based	population based	the ABI 7700 Sequence Detection System or ‘‘Taqman’’ assay	1437(761)	(0.16/0.14)	offsets and age
Pachkowski, B. F.	2006	USA	Whites	population based	population based	the ABI 7700 sequence detection system or ‘‘Taqman’’ assay	2366(1244)	(0.36/0.35)	offsets and age
Thyagarajan, B.	2006	USA	NS	population based	population based	PCR-RFLP, PCR-invader, or Sequenom	515(193)	(0.50/0.36)	age, family history of breast cancer among first degree relatives, physical activity, body mass index
Zhai, X.	2006	China	Han Chinese	hospital based	population based	PCR-RFLP	941(302)	( 0.25/0.26)	age, age at menarche and menopausal
									status
Zhang, Y.	2006	USA	Caucasian	population based	population based	TaqMan	5626(3039)	(0.36/0.36)	age and study site
Zhang, Y.	2006	Poland	Caucasian	population based	population based	TaqMan	4275(1985)	(0.37/0.35)	age and study site
Costa, S.	2007	Portugal	NS	hospital based	population based	PCR-RFLP	917(256)	(0.34/0.39)	age
Silva, S. N.	2007	Portugal	Caucasian Portuguese	hospital based	hospital based	PCR-RFLP	697(241)	(0.31/0.34)	age at diagnosis; alcohol consumption and smoking habits
Ali, M. F.	2008	USA	NS	hospital based	hospital based	PCR-RFLP	88(40)	(0.52/0.53)	-
Kipikasova, L.	2008	Slovakia	Caucasian	hospital based	volunteers	PCR with confronting two pair primers (PCR-CTPP)	227(114)	(0.35/0.34)	-
Loizidou, M. A.	2008	Greece	Greek Cypriots	population based	volunteers	PCR-RFLP	2283(1107)	( 0.32/0.33)	-
Mitra, A. K.	2008	India	Caucasians of Indo-Europeans	hospital based	volunteers	PCR-RFLP	375(150)	(0.39/0.53)	-
Saadat, M.	2008	Iran	Persian Muslims	hospital based	NS	PCR-RFLP	373(186)	(0.36/0.32)	family history of breast cancer in first-degree relatives and cigarette smoking
Sangrajrang, S.	2008	Thailand	NS	hospital based	hospital based	rapid capillary PCR followed by melting curve analysis using fluorescence labeled hybridization probes in a Light Cycler	931(507)	(0.27/0.23)	age, body mass index, age at menarche, family history of breast cancer, reproduction parameters, use of contraceptives, menopausal status, hazardous lifestyle, and education
Smith, T. R.	2008	USA	Caucasian	hospital based	hospital based	mass ARRAY system	718(312)	(0.34/0.33)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Smith, T. R.	2008	USA	African-American	hospital based	hospital based	mass ARRAY system	126(52)	(0.14/0.11)	age, family history of breast cancer, smoking history, age at menarche, age at first live birth and body mass index
Sobczuk, A.	2009	Poland	NS	NS	NS	PCR-RFLP	256(150)	(0.55/0.57)	-
Syamala, V. S.	2009	India	NS	hospital based	hospital based	PCR-RFLP	726(359)	(0.37/0.30)	age
Jakubowska, A.	2010	Poland	NS	NS	NS	PCR-based fragment analyses using the CEQTM 8000 Genetic DNA Analysis System and/or ethidium bromide-stained agarose gels	607(317)	(0.35/0.39)	index, year of birth and BRCA1 mutation
Jelonek, K.	2010	Poland	Caucasian	hospital based	hospital based	PCR-RFLP	645(94)	(0.35/0.37)	-
Ming-Shiean, H.	2010	Taiwan	NS	hospital based	hospital based	real-time PCR with the TaqMan assay system	926(395)	(0.31/0.29)	age, age at first full-term pregnancy, cigarette smoking, body mass index, and family history
Romanowicz, H.	2010	Poland	NS	hospital based	NS	PCR-RFLP	440(220)	(0.44/0.34)	-
Santos, R. A.	2010	Brazil	NS	NS	volunteers	PCR-RFLP	150(65)	(0.33/0.40)	-
Sterpone, S.	2010	Italy	Caucasian	hospital based	volunteers	PCR-RFLP	74(43)	(0.53/0.32)	-
Zipprich, J.	2010	USA	NS	population based	population based	fluorescence polarization method	555(254)	(0.30/0.34)	menopause and age and age at menarche
Liu, L.	2011	China	Han Chinese	hospital based	population based	PCR-RFLP	1999(995)	(0.26/0.28)	age
Roberts, M. R.	2011	USA	NS	hospital based	population based	matrix-assisted laser desorption ionization time-of-flightmass spectrometry	2752(982)	(0.34/0.32)	age, education, race, smoking status, age at first birth, family history of breast cancer, history of benign breast disease, age at menarche and hormone therapy use
Hussien, Y. M.	2012	Egypt	NS	hospital based	hospital based	PCR-CTPP method	200(100)	(0.37/0.30)	age
Al Mutairi, F. M.	2013	Saudi Arabia	NS	hospital based	hospital based	TaqMan SNP genotype analysis technique	200(100)	(0.26/0.25)	-
Przybylowska-Sygut, K.	2013	Poland	NS	hospital based	population based	PCR-RFLP	390(185)	(0.36/0.37)	-
Sapkota, Y.	2013	Canada	Caucasian	population based	population based	sequenom iPLEX Gold platform	7225(2720)	NC	body mass index
Ding, p.	2014	China	Han Chinese	hospital based	hospital based	polymerase chain reaction-ligase detection reaction (PCR-LDR)	1239(606)	(0.30/0.25)	Age
Luo, H.	2014	China	Han Chinese	hospital based	hospital based	PCR-CTPP	439(194)	(0.34/0.25)	age, age at menarche, pregnancy, menopausal status, body mass index, and family history of cancer
McCullough, L. E.	2014	USA	White and Black	population based	population based	fluorescence polarization (FP) method	1163(597)	NC	age
Ramadan, R. A.	2014	Egypt	NS	hospital based	hospital based	PCR-RFLP	175(100)	(0.44/0.29)	age, body mass index, parity, menopausal status, age at menarche, family history of breast cancer,
									smoking, or physical activity
Shadrina, A. S	2014	Russia	Caucasian	NS	hospital based	real-time PCR allelic discrimination with TaqMan probes	1222(658)	(0.36/0.31)	age
Macias-Gomez, N. M.	2015	Mexico	NS	hospital based	NS	PCR-RFLP	686(345)	(0.29/0.23)	-

a Definitions: Date, year of publication; NS, not state; NC, not calculable; USA, United State of America.

b Confounders in multivariate analysis.

Appendix A9.

Characteristics of Literature Included in the Systematic Review Evaluating the Association Between OGG1 Ser326Cys Polymorphism and Breast Cancer a

First Author	Date	Country	Ethnicity	Study Design	Control Source	Genotyping Methods	Analyzed Sample Size (Case Number)	Minor Allele Frequency (Case/Control)	Considered Confounders [b]
Choi, J. Y.	2003	Korea	Korean	hospital based	hospital based	PCR-RFLP	549 (265)	(0.56/0.55)	age, body mass index, family history of breast cancer, and parity factor
Choi, J. Y.	2003	Japan	Japanese	hospital based	hospital based	PCR-CTPP	389 (201)	(0.48/0.42)	age, body mass index , family history of breast cancer, and parity factor
Vogel, U.	2003	Denmark	NS	population based	population based	real-time PCR	869 (425)	(0.22/0.24)	-
Huang, C. S.	2004	Taiwan	NS	hospital based	hospital based	PCR-RFLP	368 (136)	(0.58/0.60)	-
Cai, Q.	2006	China	Han Chinese	population based	population based	fluorogenic 5´ nuclease assay with primers	2269 (1102)	(0.58/0.58)	AGE, education level, menopausal status, and age at first live birth
Rossner, P., Jr.	2006	USA	NS	population based	population based	template-directed dye-terminator incorporation assay with fluorescence polarization detection	2134 (1041)	(0.22/0.22)	AGE and simultaneously adjusted for three SNPs
Zhang, Y.	2006	USA	Caucasian	population based	population based	TaqMan	2815 (1571)	(0.21/0.21)	age and study site
Romanowicz-Makowska, H.	2008	Poland	NS	NS	NS	PCR-RFLP	206 (100)	(0.51/0.57)	-
Sangrajrang, S.	2008	Thailand	NS	hospital based	hospital based	rapid capillary PCR followed by melting curve analysis using fluorescence labeled hybridization probes in a Light Cycler	930 (506)	(0.55/0.50)	age, body mass index, age at menarche, family history of breast cancer, reproduction parameters, use of contraceptives, menopausal status, hazardous lifestyle, and education
Synowiec, E.	2008	Poland	NS	hospital based	NS	PCR-RFLP	89 (41)	(0.52/0.68)	age
Ming-Shiean, H.	2010	Taiwan	NS	hospital based	hospital based	real-time PCR with the TaqMan assay system	934 (401)	(0.63/0.62)	age, age at first full-term pregnancy, cigarette smoking, body mass index, and family history
Sterpone, S.	2010	Italy	Caucasian	hospital based	volunteers	PCR-RFLP	74 (43)	(0.31/0.29)	-
Roberts, M. R.	2011	USA	NS	hospital based	population based	matrix-assisted laser desorption ionization time-of-flightmass spectrometry	2941 (1054)	(0.22/0.22)	age, education, race, smoking status, age at first birth, family history of breast cancer, history of benign breast disease, age at menarche and hormone therapy use
Kim, K. Y.	2013	Korea	NS	hospital based	population based	Illumina GoldenGate™method	707 (346)	(0.47/0.49)	age, family history and body mass index
Xie, H.	2013	China	Han Chinese	hospital based	hospital based	PCR and high-resolution melting and sequencing	1407 (630)	(0.60/0.56)	-
Luo, H.	2014	China	Han Chinese	hospital based	hospital based	PCR-CTPP	439 (194)	(0.55/0.59)	age, age at menarche, pregnancy, menopausal status, body mass index, and family history of cancer
McCullough, L. E.	2014	USA	White and black	population based	population based	fluorescence polarization (FP) method	1135(580)	NC	age
Rodrigues, P.	2014	Spain	Caucasian	hospital based	hospital based	SNPlex technology (based on oligation assay/polymerase chain reaction and capillary electrophoresis)	2123(711)	(-/0.21)	age
Smolarz, B.	2014	Poland	NS	hospital based	hospital based	PCR-RFLP	140 (70)	(0.49/0.50)	-

b Confounders in multivariate analysis.