REVIEW

Genetic polymorphisms and breast cancer risk: evidencefrom meta-analyses, pooled analyses, and genome-wideassociation studies

Sihua Peng • Bingjian Lu • Wenjing Ruan •

Yimin Zhu • Hongqiang Sheng • Maode Lai

Received: 14 February 2011 / Accepted: 15 March 2011 / Published online: 29 March 2011

� Springer Science+Business Media, LLC. 2011

Abstract To address the association between variants and

breast cancer, an increasing number of articles on genetic

association studies, genome-wide association studies

(GWASs), and related meta- and pooled analyses have been

published. Such studies have prompted an updated assess-

ment of the associations between gene variants and breast

cancer risk. We searched PubMed, Medline, and Web of

Science and retrieved a total of 87 meta- and pooled anal-

yses, which addressed the associations between 145 gene

variants and breast cancer. Analyses met the following

criteria: (1) breast cancer was the outcome, (2) the articles

were all published in English, and (3) in the recent pub-

lished meta- and pooled analyses, the analyses with more

subjects were selected. Among the 145 variants, 46 were

significantly associated with breast cancer and the other 99

(in 62 genes) were not significantly associated with breast

cancer. The summary ORs for the 46 significant associa-

tions (P \ 0.05) were further assessed by the method of

false-positive report probability (FPRP). Our results

demonstrated that 10 associations were noteworthy: CASP8

(D302H), CHEK2 (*1100delC), CTLA4 (?49G[A),

FGFR2 (rs2981582, rs1219648, and rs2420946), HRAS

(rare alleles), IL1B (rs1143627), LSP1 (rs3817198), and

MAP3K1 (rs889312). In addition, eight GWASs were

identified, in which 25 loci were obtained (14 in nine genes,

six near a gene or genes, and five intergenic loci). Of

the 25 SNPs, 20 were noteworthy: C6orf97 (rs2046210

and rs3757318), FGFR2 (rs2981579, rs1219648, and

rs2981582), LSP1 (rs909116), RNF146 (rs2180341),

SLC4A7 (rs4973768), MRPS30 (rs7716600), TOX3

(rs3803662 and rs4784227), ZNF365 (rs10995190),

rs889312, rs614367, rs13281615, rs13387042, rs11249433,

rs1011970, rs614367, and rs1562430. In summary, in this

review of genetic association studies, 31.7% of the gene-

variant breast cancer associations were significant, and

21.7% of these significant associations were noteworthy.

However, in GWASs, 80% of the significant associations

were noteworthy.

Keywords Breast cancer � Polymorphism �Meta-analysis � Genome-wide association study �Pooled analysis

Introduction

Breast cancer remains a major health problem around the

world, and its incidence continues to increase [1]. It

accounts for 26% of all new cancer cases among women in

the United States [2]. During the last few decades,

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10549-011-1459-5) contains supplementarymaterial, which is available to authorized users.

S. Peng � H. Sheng � M. Lai (&)

Department of Pathology, Zhejiang University School

of Medicine, 388 Yuhangtang Road, Hangzhou 310058,

Zhejiang, People’s Republic of China

e-mail: lmp@zju.edu.cn

B. Lu

Department of Surgical Pathology, Affiliated Women’s Hospital,

Zhejiang University School of Medicine, Hangzhou 310003,

People’s Republic of China

W. Ruan

Department of Respiratory Diseases, Affiliated Sir Run Run

Shaw Hospital, Zhejiang University School of Medicine,

Hangzhou 310016, People’s Republic of China

Y. Zhu

Department of Public Health, School of Medicine, Zhejiang

University, Hangzhou 310058, People’s Republic of China

123

Breast Cancer Res Treat (2011) 127:309–324

DOI 10.1007/s10549-011-1459-5

extensive efforts have identified sources of genetic sus-

ceptibility to breast cancer. With the help of continuing

technological advances, many observational studies eval-

uating the association between variants in different genes

and breast cancer risk have been published [3]. These

studies prompted us to estimate their general contribution

to our current understanding of the genetic susceptibility to

breast cancer [4, 5].

One of the drawbacks of genetic epidemiology is that the

results are not easily replicated. Many studies attempting to

explore previously published, statistically significant find-

ings for the variants have failed to repeat those findings,

suggesting that false-positive results may exist in certain

studies [6]. Recently, Burton et al. [7] emphasized that if

sample sizes are large enough, it is possible to identify and

replicate genetic associations with common complex dis-

eases. Therefore, the size issue is an important methodo-

logical concern in genetic association studies [8].

Meta-analysis was defined by Glass [9] as ‘‘the statis-

tical analysis of a large collection of analysis results from

individual studies for the purpose of integrating the find-

ings.’’ Blettner [10] gave a definition of pooled analysis:

‘‘prospectively planned, pooled analysis of several studies,

where pooling is already a part of the protocol. Data col-

lection procedures, definition of variables, questions and

hypotheses are standardized for the individual studies.’’

Clearly, meta-analyses and pooled analyses are very good

tools for combining results from different studies. To this

end, we collected evidence from articles published on

meta-analyses and pooled analyses to assess the associa-

tions between breast cancer risk and various polymor-

phisms [11].

As early as 1999, Dunning et al.[12] published a sys-

tematic review of the associations of genetic variants and

breast cancer risk, in which 46 case–control studies

involving 18 genes were identified. They reported 12

variants to be significantly associated with breast cancer

risk. Since then, more articles on the associations between

variants and breast cancer risk have been published.

In 2008, Dong et al. [13] published another insightful

systematic review on the associations between genetic

variants and cancer risk, in which 20 variants were reported

to be significantly related to breast cancer risk. However,

only 3 years later, more than 100 new meta-analyses and

pooled analyses have been published, including more than

90 meta-analyses published in 2010 and 2011. Thus, the

results have been thoroughly updated. To this end, an

updated and systematic review on this issue is urgently

needed to evaluate the associations between genetic vari-

ants and breast cancer risk.

In contrast, a genome-wide association study (GWAS)

examines all or most of the genes in the genome of dif-

ferent individuals of a particular species to identify the

extent to which the genes vary from individual to indi-

vidual [14]. In humans, GWASs can identify the associa-

tions of particular genes with diseases, such as type 2

diabetes and breast cancer [15, 16]. In a GWAS, at least

100,000 single nucleotide polymorphisms (SNPs) of hun-

dreds or thousands of individuals are tested. To date, more

than 812 human GWASs have examined approximately

150 diseases and traits and have found more than 3,977

SNP associations [17, 18]. Therefore, GWASs evaluating

the associations between the various SNPs and breast

cancer risk were also reviewed in this article.

Methods

We searched PubMed, Medline, and Web of Science up to

Jan. 2011 for meta-analyses and pooled analyses involving

the associations between various polymorphisms and breast

cancer risk. We performed a literature search of the above

databases using three search themes, which were combined

using the Boolean operator ‘‘and.’’ The first theme was

(‘‘breast neoplasms’’ OR ‘‘breast cancer’’); the second was

(‘‘gene’’ OR ‘‘polymorphism’’ OR ‘‘single nucleotide

polymorphisms’’ OR ‘‘variant’’); and the third was (‘‘meta-

analysis’’ OR ‘‘pooled analysis’’).

Using this search strategy, 282, 302, and 214 articles (a

total of 798) were retrieved from PubMed, Medline, and

the Web of Science, respectively. Finally, after reading the

abstracts and/or full text, we selected 87 articles to include

in this review (Fig. 1). To be included, studies had to meet

the following criteria: (1) the outcome investigated in the

meta-analyses and pooled analyses was breast cancer; (2)

the articles were published in English; and (3) to avoid

overlap of results from more than one meta-analysis or

pooled analysis addressing the same variant, the analyses

with more subjects were selected. Data extracted from each

meta-analysis or pooled analysis included gene name,

genetic variant, OR (odds ratio) and 95% CI (confidence

interval), the number of studies, the number of subjects

(cases/controls), the test for between-study heterogeneity

(e.g., Q test [19]), and the test for publication bias (Egger’s

test [20]).

In this review, associations were considered statistically

significant if the reported P value was\0.05 or if the 95%

CI exceeded 1.0. For each statistically significant associa-

tion reported, we estimated the false-positive report prob-

ability (FPRP) using the method described by Wacholder

et al. [21] The magnitude of the FPRP is determined by

three parameters: (1) prior probability p of a true associa-

tion of the tested genetic variant with a disease, (2) a level

or observed P value, and (3) statistical power to detect the

OR of the alternative hypothesis at the given a level or

P value. They suggested estimating statistical power based

310 Breast Cancer Res Treat (2011) 127:309–324

123

on the ability to detect an OR of 1.5, with an a level equal

to the observed P value. Because much smaller ORs were

reported recently, we think that this estimate is too con-

servative. Therefore, ORs of both 1.5 and 1.2 were chosen

to present results. To assess whether the associations were

noteworthy, an FPRP cutoff value of 0.2 was used [21].

Thereby FPRP values less than 0.2 indicate an association

and are referred to as noteworthy in this review. Statistical

power and FPRP values were calculated by the Excel

spreadsheet which can be downloaded from the web site of

Wacholder et al. [21].

GWAS articles were obtained by searching PubMed and

checking the web site at www.genome.gov/gwastudies

[18]. The inclusion/exclusion criteria were as follows: (1)

only significant associations between various SNPs and

breast cancer risk were included, with a cutoff value of

P \ 1910-5 and (2) if the OR and CI were not reported,

this GWAS was excluded. FPRP methodology was also

used to evaluate the significant associations.

Results

We identified 87 published articles with meta-analyses and

pooled analyses, encompassing 80 different genes. These 87

meta-analyses and pooled analyses addressed associations

between 145 gene variants and breast cancer, including 40

meta-analyses and pooled analyses that reported significant

associations between 46 variants (in 35 genes) and breast

cancer (Table 1) and 61 meta-analyses and pooled analyses

that reported non-significant associations between 99 vari-

ants (in 62 genes) and breast cancer (Tables S1 and S2). Of

these 87 articles, 76 (87.4%) were published after 2009, 67

(77.0%) after 2010, and 6 (6.9%) in 2011.

Significant gene-variant breast cancer associations

Among the 145 gene-variant breast cancer associations that

were evaluated, the summary ORs for 46 (31.7%) associ-

ations were statistically significant (Fig. 2; Table 1).

Inverse associations for the variants were found in 8

of these 46 associations: CASP8 (D302H) [22], ESR1

(rs2234693 and rs1801132) [23], ESR2 (rs4986938) [24],

NBS1 (8360G[C) [25], NOS3 (-786T[C) [26], TP53

(rs1042522) [27], and XRCC3 (rs1799796) [28], with a

mean OR of 0.86 (median, 0.89; range, 0.66-0.95). The

other 38 analyses reported ORs higher than 1.0, with a mean

OR of 1.39 (median, 1.15; range, 1.05–3.13) (Table 1).

To evaluate the robustness of these findings, we calcu-

lated FPRP values. Among the 46 significant associations,

36 gene-variant breast cancer associations had FPRP values

[0.2 at the pre-specified prior probability of 0.001; these

associations were not considered noteworthy. For example,

although the OR from the pooled analysis for CCND1

(G870A) indicated a significant association with breast

cancer (OR, 1.09; 95% CI, 1.01–1.18), FPRP values were

higher than 0.2; therefore, this result was not considered

noteworthy. At a prior probability level of 0.001 and sta-

tistical power to detect an OR of 1.5, ten associations were

noteworthy (FPRP \ 0.2): CASP8 (D302H), CHEK2

Records excluded (n=123)

Full-text articles excluded, with reasons: Not meta-analysis/pooled-analysis,

genetic polymorphism, or breast cancer (n1=42).

Not latest meta-analysis (n2=19).

Total n=61

Records screened by title (n=393)

Records screened by abstract (n=270)

Records excluded (n=122)

Additional records identified through other sources

(n=0)

Records identified through database searching (PubMed 282, Medline 302,

and Web of Science 214) (n=798)

Meta-analyses and pooled analyses (n=87)

Records excluded (n=405)

Records after overlaps removed (n=798)

Full-text articles assessed for eligibility (n=148)

Fig. 1 Selection of studies

Breast Cancer Res Treat (2011) 127:309–324 311

123

Table 1 Statistically significant gene-variant breast cancer overall associations and false-positive report probabilities (FPRP)

Gene (variant) Year Comparison OR (95% CI) Publication

bias/

heterogeneity

No. of

studies

Cases/control Power

OR,

1.5

Power

OR,

1.2

FPRP values

at prior

probability

of 0.001

Ref.

OR

1.5

OR

1.2

CASP8 (D302H) 2010 Dominant

model

0.87 (0.83–0.92) No/0.66 4 39,109 (18,791/20,318) 1.0 0.935 0.001 0.001 [22]

CCND1 (G870A) 2010 AA vs. GG 1.09 (1.01–1.18) 0.054/0.122 13 21,082 (9,911/11,171) 1.0 0.991 0.971 0.971 [145]

CHEK2

(*1100delC)

2008 Heterozygotes

vs.

noncarrier

2.40 (1.80–3.20) 0.08/NA 12 36,909 (18,329/18,580) 0.001 \0.000 0.004 0.678 [95]

CTLA4 (?49G[A) 2010 Dominant

model

1.24 (1.18–1.32) 0.998/0.11 32 24,452 (11,273/13,179) 1.0 0.152 0.000 0.000 [146]

CYP19 ((TTTA)10) 2010 (TTTA)10 1.52 (1.12–2.06) 0.58/0.06 14 16,104 (7,743/8,361) 0.466 0.064 0.937 0.991 [42]

ERCC2

(Lys751Gln)

2010 Dominant

model

1.13 (1.02–1.24) 0.036/0.0001 32 29,897 (14,545/15,352) 1.0 0.898 0.908 0.917 [47]

ESR1 (rs2234693) 2010 Dominant

model

0.95 (0.89–1.00) 0.97/0.39 11 26,920 (10,300/16,620) 1.0 1.0 0.980 0.980 [23]

(rs1801132) Dominant

model

0.92 (0.85–0.99) 0.86/0.66 7 12,505 (5,649/6,856) 1.0 0.996 0.963 0.963 [23]

ESR2 (rs4986938) 2010 Dominant

model

0.94 (0.90–0.99) 0.748/0.379 9 26,858 (10,837/16,021) 1.0 1.0 0.951 0.951 [24]

FGFR2 (rs2981582) 2010 GG vs. AA 1.48 (1.35–1.61) [0.05/

\ 0.000

11 91,890 (40,292/51,598) 0.0 0.0 0.000 0.000 [147]

(rs1219648) GG vs. AA 1.50 (1.36–1.65) [0.05/0.09 9 58,903 (13,930/44,973) 0.548 0.0 0.000 0.000 [147]

(rs2420946) CC vs. TT 1.52 (1.37–1.68) [0.05/0.20 6 15,329 (6,781/8,548) 0.413 0.0 0.000 0.000 [147]

GSTM1 (null/

present)

2010 Null vs.

present

1.10 (1.04–1.16) 0.445/0.003 59 46,281 (20,993/25,288) 1.0 0.999 0.303 0.303 [148]

GSTT1 (null/

present)

2010 Null vs.

present

1.11 (1.04–1.20) 0.048/0.059 41 36,584 (16,589/19,995) 1.0 0.975 0.897 0.899 [53]

For Chinese 2010 Null vs.

present

1.06 (0.87–1.29) 0.742/0.017 8 5,980 (2,450/3,530) – – – – [53]

HER2 (Ile655Val) 2010 Dominant

model

1.10 (1.01–1.20) 0.03/0.01 27 24,042 (11,504/12,538) 1.0 0.975 0.969 0.970 [149]

HRAS (rare alleles) 1997 Per-allele

comparison

2.7 (2.10–3.40) NA/NA 8 1,631 (694/937) 0.0 0.0 0.000 0.000 [150]

IGFBP3 (A-202C) 2010 CC vs. AA 1.06 (1.02–1.11) 0.63/0.60 27 78,811 (33,557/45,254) 1.0 1.0 0.93 0.93 [96]

IL1B (rs1143627) 2010 Recessive

model

1.40 (1.17–1.67) No/0.49 4 2,708 (1,543/1,165) 0.778 0.043 0.191 0.809 [58]

LSP1 (rs3817198) 2010 Allele contrast 1.06 (1.04–1.08) No/0.166 7 69,591 (33,920/35,671) 1.0 1.0 0.000 0.000 [151]

MAP3K1(rs889312) 2010 Dominant

model

1.10 (1.06–1.13) 0.696/0.43 7 59,977 (26,015/33,962) 1.0 1.0 0.000 0.000 [152]

MDM2(SNP309) 2010 GT vsTT 1.06 (1.00–1.12) No/0.77 16 25,979 (12,986/12,993) 1.0 1.0 0.974 0.974 [153]

For non-Chinese 2010 G vs. T 1.02 (0.97–1.08) No/No 13 23,652 (12,094/11,558) – – – – [153]

MMP-2 (-1306

C/T)

2010 CC vs.

CT ? TT

1.27 (1.10–1.47) No/\0.001 4 3,243 (1,590/1,653) 0.987 0.224 0.579 0.859 [61]

MTHFR (C667T) 2010 TT vs.

CC ? CT

1.12 (1.01–1.24) No/0.001 41 38,868 (16,480/22,388) 1.0 0.908 0.967 0.970 [154]

NBS1 (657del5) 2006 Carrier vs.

noncarrier

3.13 (1.40–7.00) No/No 2 2,406 (1,620/786) 0.037 0.010 0.993 0.993 [97]

(8360G[C) 2010 CC vs. GG 0.75 (0.74–0.98) 0.20/0.13 10 10,117 (4,452/5,665) 0.806 0.220 0.977 0.994 [25]

NOS3 (eNOS,894

G[T)

2010 TT vs. GG 1.29 (1.06–1.56) 0.110/0.223 11 9,507 (4,665/4,842) 0.94 0.228 0.902 0.974 [155]

(-786T[C) 2010 Dominant

model

0.66 (0.47–0.94) No/0.741 3 3,326 (1,856/1,470) 0.478 0.098 0.978 0.995 [26]

POR (Gly5Gly) 2007 GG vs. AA 1.58 (1.04–2.41) NA/0.85 4 1,915 (1,038/877) 0.405 0.101 0.988 0.997 [98]

312 Breast Cancer Res Treat (2011) 127:309–324

123

(*1100delC), CTLA4 (?49G[A), FGFR2 (rs2981582,

rs1219648, and rs2420946), HRAS (rare alleles), IL1B

(rs1143627), LSP1 (rs3817198), and MAP3K1 (rs889312).

This number was further reduced to eight when we cal-

culated the statistical power based on a lower OR of 1.2:

CASP8 (D302H), CTLA4 (?49G[A), FGFR2 (rs2981582,

rs1219648, and rs2420946), HRAS (rare alleles), LSP1

(rs3817198), and MAP3K1 (rs889312).

Non-significant gene-variant breast cancer associations

Besides the statistically significant associations, statisti-

cally non-significant associations were reported for 99

variants (with P values [ 0.05; Table S1) [22–24, 29–86].

Of these 99 associations, 49 (49.5%) were inverse associ-

ations for the variants, with a mean OR of 0.91 (median,

0.96; range, 0.39–0.99).

Table 1 continued

Gene (variant) Year Comparison OR (95% CI) Publication

bias/

heterogeneity

No. of

studies

Cases/control Power

OR,

1.5

Power

OR,

1.2

FPRP values

at prior

probability

of 0.001

Ref.

OR

1.5

OR

1.2

RAD51 (135G[C) 2010 Recessive

model

1.35 (1.05–1.74) 0.16/0.06 9 26,444 (13,241/13,203) 0.722 0.182 0.963 0.991 [156]

SULT1A1 (R213H) 2011 Dominant

model

1.13 (1.01–1.26) No/0.001 16 23,445 (9,881/13,564) 1.0 0.86 0.965 0.970 [157]

For Asians 2011 Dominant

model

1.11 (0.94–1.32) No/0.004 7 23,445 (4,966/7,770) – – – – [157]

TGFB1 (rs1982073) 2010 Dominant

model

1.05 (1.01–1.10) 0.675/0.195 30 47,817 (20,401/27,416) 1.0 1.0 0.975 0.975 [99]

TNF (rs1800629) 2009 Recessive

model

1.10 (1.04–1.17) \0.05/0.45 11 23,095 (10,184/12,911) 1.0 0.997 0.996 0.996 [158]

TOX3 (rs3803662) 2010 Dominant

model

1.15 (1.00–1.32) 0.249/0.000 8 29,505 (25,828/36,177) 1.0 0.727 0.979 0.985 [67]

TP53 (rs1042522) 2010 Dominant

model

0.90 (0.82–0.99) 0.015/0.001 39 55,720 (26,041/29,679) 1.0 0.943 0.968 0.970 [27]

(rs17878362) 2010 Ins/Ins vs. Del/

Del

1.81 (1.30–2.52) No/0.30 9 5,310 (2,715/2,595) 0.133 0.007 0.768 0.983 [159]

TYMS (TSER) 2010 2R/2R vs. non-

2R/

non-2R

1.30 (1.10–1.53) NA/0.74 6 6,141 (2,718/3,423) 0.957 0.168 0.625 0.905 [69]

VDR (FokI) 2009 ff vs. FF 1.14 (1.03–1.27) No/0.006 13 29,880 (12,464/17,416) 1.0 0.824 0.946 0.955 [71]

WDR79

(rs2287499)

2007 GG vs. CC 1.60 (1.04–2.47) NA/0.44 2 6,059 (2,692/3,367) 0.385 0.097 0.989 0.997 [31]

(rs2287498) 2007 CT vs. CC 1.15 (1.00–1.32) NA/0.19 2 6,009 (2,655/3,354) 1.0 0.727 0.979 0.985 [31]

XRCC1

(Arg399Gln)

2009 Gln/Gln vs.

Arg/Arg

1.14 (1.01–1.29) 0.006/0.002 37 46,386 (22,481/23, 905) 1.0 0.792 0.974 0.979 [100]

For Asians

(Arg399Gln)

2009 Recessive

model

1.26 (0.96–1.64) 0.006/0.571 4 3,275 (1,573/1,702) – – – – [100]

(-77T[C) 2011 CC vs. TT 2.55 (1.11–5.86) No/No 7 6,065 (2,888/3,177) 0.106 0.038 0.996 0.999 [160]

XRCC3

(rs1799794)

2010 Dominant

model

1.09 (1.01–1.17) No/0.15 4 12,866 (6,303/6,563) 1.0 0.996 0.945 0.945 [28]

(rs1799796) 2010 GG vs. AA 0.86 (0.77–0.96) No/0.21 4 12,952 (6,270/6,682) 1.0 0.713 0.878 0.910 [28]

(rs861539) 2010 TT vs. CC 1.07 (1.01–1.14) 0.116/0.200 23 43,028 (20,791/22,237) 1.0 1.0 0.979 0.979 [161]

For Chinese

(rs861539)

2010 TT vs. CC 0.57 (0.34–0.98) NA/0.819 3 2,328 (1,216/1,112) 0.285 0.085 0.993 0.998 [161]

ZNF350 (D35D) 2006 TC vs. TT 1.10 (1.01–1.20) NA/0.09 2 2,950 (1,554/1,396) 1.0 0.975 0.969 0.970 [86]

(S472P) 2006 Ser/Pro vs.

Ser/Ser

1.24 (1.05–1.48) NA/0.01 2 5,101 (2,719/2,382) 0.983 0.358 0.946 0.980 [86]

NA not available, No significant publication bias/heterogeneity was not found

Breast Cancer Res Treat (2011) 127:309–324 313

123

The other 50 analyses reported ORs higher than 1.0,

with a mean value of 1.08 (median, 1.04; range, 1.00–1.64)

(Table S1).

Significant gene-variant breast cancer associations

in genome-wide association studies (GWASs)

By searching PubMed and checking the web site (A Cat-

alog of Genome-Wide Association Studies), eight GWASs

were identified [11, 87–93], which included 25 SNPs, with

a threshold of P values \1 9 10-5 (Table 2; Fig. 3). Of

these 25 SNPs, 14 were in 9 genes, six were near a gene or

genes, and five were intergenic. These 25 associations

reported ORs [ 1.0, with a mean value of 1.19 (median,

1.17; range, 1.04–1.43). At a prior probability level of

0.001 and statistical power to detect an OR of 1.5/1.2, 20

SNPs maintained noteworthy associations with breast

cancer (FPRP \ 0.2) (Table 2).

Discussion

We found that 31.7% of all the gene-variant breast cancer

associations from published meta-analyses and pooled

analyses were statistically significant. With continuous

progress in genotyping technologies, a large number of

genetic variants can be tested. Therefore, many false-

positive results are likely to be published due to the widely

used significance threshold of P \ 0.05. Therefore, we also

used FPRP methodology, as did Dong et al. in their sys-

tematic review. FPRP is based not only on the observed

P value but also on both the power and the prior probability

of the hypothesis. By using this method, we can integrate

the prior knowledge of the tested variants [13]. Thomas and

Clayton [94] suggested that the prior probability will usu-

ally exceed 0.001. Thus, at a prior probability of 0.001,

only 10 of the associations were noteworthy, and these 10

may be true associations.

CASP8CCND1CHEK2CTLA4CYP19ERCC2ESR1ESR1ESR2FGFR2FGFR2FGFR2GSTM1GSTT1HER2HRASIGFBP3IL1BLSP1MAP3K1MDM2MMP-2MTHFRNBS1NBS1NOS3NOS3PORRAD51SULT1A1TGFB1TNFTNRC9TP53TP53TYMSVDRWDR79WDR79XRCC1XRCC1XRCC3XRCC3XRCC3ZNF350ZNF350

Gene

D302HG870A*1100delC+49G > A(TTTA)10Lys751Glnrs2234693rs1801132rs4986938rs2981582rs1219648rs2420946null/presentnull/presentIle655Valrare allelesA-202Crs1143627rs3817198rs889312SNP309-1306 C/TC667T657del58360G>C894 G>T-786T>CGly5Gly135G>CR213Hrs1982073rs1800629rs3803662rs1042522rs17878362TSERFokIrs2287499rs2287498Arg399Gln-77T>Crs1799794rs1799796rs861539D35DS472P

Polymorphism

2010201020082010201020102010201020102010201020102010201020101997201020102010201020102010201020062010201020102007201020102010200920102010201020102009200720072009201020102010201020062006

Year

39,10921,08236,90924,45216,10429,89726,92012,50526,85891,89058,90315,32946,28136,58424,0421,63178,8112,70833,92059,97725,9793,24338,8682,40610,1179,5073,3261,91526,44423,44547,81723,09529,50555,7205,3106,14129,8806,0596,00946,3861,99912,86612,95243,0282,9505,101

Subjects

0.87 (0.82, 0.92)1.09 (1.01, 1.18)2.40 (1.80, 3.20)1.24 (1.16, 1.32)1.52 (1.12, 2.06)1.13 (1.03, 1.24)0.95 (0.90, 1.00)0.92 (0.85, 0.99)0.94 (0.89, 0.99)1.48 (1.36, 1.61)1.50 (1.36, 1.65)1.52 (1.38, 1.68)1.10 (1.04, 1.16)1.11 (1.03, 1.20)1.10 (1.01, 1.20)2.70 (2.14, 3.40)1.06 (1.01, 1.11)1.40 (1.17, 1.67)1.06 (1.04, 1.08)1.10 (1.07, 1.13)1.06 (1.00, 1.12)1.27 (1.10, 1.47)1.12 (1.01, 1.24)3.13 (1.40, 7.00)0.75 (0.57, 0.98)1.29 (1.07, 1.56)0.66 (0.46, 0.94)1.58 (1.04, 2.41)1.35 (1.05, 1.74)1.13 (1.01, 1.26)1.05 (1.00, 1.10)1.10 (1.03, 1.17)1.15 (1.00, 1.32)0.90 (0.82, 0.99)1.81 (1.30, 2.52)1.30 (1.10, 1.53)1.14 (1.02, 1.27)1.60 (1.04, 2.47)1.15 (1.00, 1.32)1.14 (1.01, 1.29)2.55 (1.11, 5.86)1.09 (1.02, 1.17)0.86 (0.77, 0.96)1.07 (1.00, 1.14)1.10 (1.01, 1.20)1.24 (1.04, 1.48)

ratio (95% CI)Odds

1.2 1 1.5

Fig. 2 Risk of breast cancer

and genetic variants, as

evaluated in meta-analyses and

pooled analyses with significant

summary risk estimates

314 Breast Cancer Res Treat (2011) 127:309–324

123

Table 2 Genetic variants of breast cancer risk obtained from GWASs

Gene (variant) Year Variants OR (95% CI) MAFb P valuec Cases/control Power

OR,

1.5

Power

OR,

1.2

FPRP values

at prior

probability of

0.001

Ref.

OR,

1.5

OR,

1.2

C6orf97 2009 rs2046210 1.29

(1.21–1.37)

0.37 2 9 10-15 3,027 (1,505/

1,522)

1.0 0.018 0.000 0.000 [88]

2010 rs3757318 1.3

(1.17–1.46)

0.07 3 9 10-6 8,556 (3,659/

4,897)

0.992 0.088 0.009 0.096 [87]

FGFR2 2010 rs2981579 1.43

(1.35–1.53)

0.42 4 9 10-31 8,556 (3,659/

4,897)

0.866 0.000 0.000 0.000 [87]

2007 rs2981582 1.26

(1.23–1.30)

0.38 2 9 10-76 754 (390/364) 1.0 0.06 0.000 0.000 [11]

2010 rs1219648 1.32

(1.22–1.42)

0.42 2 9 10-13 8,428 (2,702/

5,726)

1.0 0.005 0.000 0.000 [92]

LSP1 2007 rs3817198 1.07

(1.04–1.11)

0.3 3 9 10-9 754 (390/364) 1.0 1.0 0.232 0.232 [11]

2010 rs909116 1.17

(1.10–1.24)

0.53 7 9 10-7 8,556 (3,659/

4,897)

1.0 0.803 0.000 0.000 [87]

SLC4A7 2010 rs4973768 1.16

(1.10–1.24)

0.47 6 9 10-7 8,556 (3,659/

4,897)

1.0 0.840 0.013 0.015 [87]

MRPS30 2010 rs7716600 1.24

(1.14–1.34)

0.23 7 9 10-7 8,428 (2,702/

5,726)

1.0 0.204 0.000 0.000 [92]

RNF146 2008 rs2180341 1.41

(1.25–1.59)

0.21 3 9 10-8 548 (249/299) 0.844 0.004 0.000 0.005 [89]

TOX3 2007 rs3803662 1.28

(1.21–1.35)

0.27 6 9 10-19 13,145 (1,599/

11,546)

1.0 0.017 0.000 0.000 [90]

2010 rs4784227 1.24

(1.20–1.29)

0.24 1 9 10-28 4,157 (2,073/

2,084)

1.0 0.11 0.000 0.000 [93]

ZNF365 2010 rs10995190 1.16

(1.10–1.22)

0.85 5 9 10-15 8,556 (3,659/

4,897)

1.0 0.906 0.000 0.000 [87]

RAD51L1 2009 rs999737 1.06

(1.01–1.14)

0.76 2 9 10-7 2,287 (1,145/

1,142)

1.0 1.0 0.991 0.991 [91]

(CDKN2A, CDKN2B) a 2010 rs1011970 1.09

(1.04–1.14)

0.17 3 9 10-8 8,556 (3,659/

4,897)

1.0 1.0 0.142 0.142 [87]

(MAP3K1)a 2010 rs889312 1.22

(1.14–1.30)

0.28 5 9 10-9 8,556 (3,659/

4,897)

1.0 0.305 0.000 0.000 [87]

2009 rs16886165 1.23

(1.12–1.35)

0.15 5 9 10-7 2,287 (1,145/

1,142)

1.0 0.302 0.013 0.042 [91]

(MYEOV, CCND1,

ORAOV1,

FGF19, FGF4,

FGF3)a

2010 rs614367 1.15

(1.10–1.20)

0.15 3 9 10-15 8,556 (3,659/

4,897)

1.0 0.975 0.000 0.000 [87]

(ZMIZ1)a 2010 rs704010 1.07

(1.03–1.11)

0.39 4 9 10-9 8,556 (3,659/

4,897)

1.0 1.0 0.232 0.232 [87]

(ANKRD16, FBXO18)a 2010 rs2380205 1.06

(1.02–1.10)

0.57 5 9 10-7 8,556 (3,659/

4,897)

1.0 1.0 0.672 0.672 [87]

Intergenic 2007 rs13281615 1.08

(1.05–1.11)

0.4 5 9 10-12 754 (390/364) 1.0 1.0 0.000 0.000 [11]

Intergenic 2009 rs11249433 1.16

(1.09–1.24)

0.39 7 9 10-10 2,287 (1,145/

1,142)

1.0 0.84 0.013 0.015 [91]

Breast Cancer Res Treat (2011) 127:309–324 315

123

In the review by Dong et al. [13], only 20 variants (in 20

genes) were reported to have significant associations with

breast cancer risk, and only three of the 20, were consid-

ered noteworthy. However, we found 46 significant asso-

ciations between the variants and breast cancer risk. Of

these 46 associations, 10 were noteworthy at a prior

probability of 0.001 and statistical power to detect an OR

of 1.5. Interestingly, 10 variants, which were reported to be

significantly associated with breast cancer risk in the

review by Dong et al., were not significantly associated

with breast cancer risk in the more recent meta-analyses (or

pooled analyses); these were ATP1B2 (-8852T?C),

COMT (Met108/158Val), CYP17 (rs4919687), CYP17

(rs4919682), CYP1A1 (A2455G), CYP1B1 (Leu432Val),

GATA3 (rs570613), PGR (PR, PROGINS), PTGS2

(Ex10?837), and TGFBR1 (*6A). Only 10 variants were

still reported to be significantly associated with breast cancer

risk based on the updated data (from novel meta-analyses or

pooled analyses); these were CASP8 (D302H) [22], CHEK2

(*1100delC) [95], CYP19 ((TTTA)10) [42], IGFBP3 (A-

202C) [96], NBS1 (657del5) [97], POR (Gly5Gly) [98],

TGFB1 (Leu10Pro) [99], WDR79 (Arg68Gly and Phe150-

Phe) [31], and XRCC1 (Arg399Gln) [100]. In addition,

another 36 polymorphisms (in 28 genes) that were signifi-

cantly associated with breast cancer risk in recent meta-

analyses or pooled analyses were added (Table 1). The

reasons for the discrepancies between results from recent

meta- and pooled analyses and previous published results

can be described as follows: (1) as technology advances,

fewer and fewer genotype errors in the subsequent individual

studies were produced; (2) with the lapse of time, the sample

sizes in the individual studies are increasing, with results of

higher power than that of previous studies; and (3) the sample

sizes in recent meta- and pooled analyses are bigger than

those of previous meta- and pooled analyses. Therefore, the

recent results are more plausible statistically than previous

results.

Recently, results from GWASs on breast cancer have

become available. In those studies, 25 SNPs (Table 2) were

significantly associated with breast cancer in eight

GWASs. Interestingly, in the eight GWASs, only three

SNPs overlapped with previous association studies, two in

FGFR2 (rs1219648 and rs2981582) and one in LSP1

(rs3817198).

Different genetic associations between polymorphisms

and breast cancer risk in various ethnic groups have been

found for many genetic variants. For example, statistically

significant associations were found between GSTT1 (null/

present), SULT1A1 (R213H), and XRCC1 (Arg399Gln)

and breast cancer risk in the overall population, but in

Asians, only non-significant associations were found

(Table 1). In particular, XRCC3 (rs861539) was signifi-

cantly associated with breast cancer in the overall popu-

lation (OR: 1.07, CI: 1.01–1.14), but an inverse (protective)

association was found in the Chinese population (OR: 0.57,

CI: 0.34–0.98) (Table 1). AURKA (T91a), ASP8 (-652

6N del), ERCC2 (Asp312Asn), HSD17B1 (Ser312Gly),

and MTR (A2756G) were not statistically associated with

breast cancer risk in overall population effects. However,

significant protective associations were found in Asians

(AURKA and ERCC2), Chinese (ASP8), Caucasians

(HSD17B1), and Europeans (MTR) (Table S1). In contrast,

CYP1A1 (A2455G), GSTP1 (Ile105Val), and MDM2

(SNP309) were not found to be significantly associated

with breast cancer risk, but significant associations were

found between breast cancer and CYP1A1 (A2455G),

GSTP1 (Ile105Val), and MTR (A2756G) in Caucasians,

Chinese, and European populations (with ORs and CI: 2.08

(1.19–3.61), 1.27 (1.01–1.61), and 0.90 (0.82–0.97)),

respectively.

Table 2 continued

Gene (variant) Year Variants OR (95% CI) MAFb P valuec Cases/control Power

OR,

1.5

Power

OR,

1.2

FPRP values

at prior

probability of

0.001

Ref.

OR,

1.5

OR,

1.2

Intergenic 2007 rs13387042 1.2

(1.14–1.26)

0.5 1 9 10-13 13,145 (1,599/

11,546)

1.0 0.50 0.000 0.000 [90]

Intergenic 2007 rs981782 1.04

(1.01–1.08)

0.53 9 9 10-6 754 (390/364) 1.0 1.0 0.977 0.977 [11]

Intergenic 2010 rs1562430 1.17

(1.10–1.25)

0.58 6 9 10-7 8,556 (3,659/

4,897)

1.0 0.773 0.003 0.004 [87]

a The SNP is near the gene/genes in bracketsb Minor Allele Frequency (MAF) in Controlsc The P values are all less than 1 9 10-5

316 Breast Cancer Res Treat (2011) 127:309–324

123

With regard to the 46 variants with statistically signifi-

cant associations, inconsistent results were reported in

many studies of the contribution of genetic polymorphisms

to breast cancer risk, e.g., of the 29 studies of TP53 (codon

72) concerning European populations, 13 ORs were greater

than 1.0 and 16 ORs were less than 1.0 [27]. In this case,

further studies are needed to clarify the contribution of

TP53 (codon 72) to breast cancer risk. From this perspec-

tive, further studies are also needed to clarify the associa-

tions between some other variants and breast cancer risk

due to many inconsistent results reported, including GSTT1

(null/present), HER2 (Ile655Val), MDM2 (SNP309),

MTHFR (C667T), RAD51 (135G[C), SULT1A1 (R213H),

TNF (rs1800629), TP53 (rs1042522 and rs17878362), VDR

(FokI), XRCC1 (Arg399Gln), and XRCC3 (rs861539).

CASP8, which was identified as noteworthy, belongs to

many key pathways, including p53 signaling, apoptosis,

and cancer [101]. CASP8 plays a important role in apop-

tosis [102]. The decreased risk for breast cancer with

CASP8 Asp302His was revealed in the pooled analysis and

has been replicated in a recent association study [103]. This

finding was also addressed in a recent meta-analysis [22].

CHEK2, located on chr22 and identified in our review as

being noteworthy at an OR of 1.5, is associated with DNA

repair, the cell cycle pathway and the p53 signaling pathway

[101]. It was reported that the protein encoded by CHEK2 is

a cell-cycle checkpoint regulator. And this protein is a

putative tumor suppressor containing a forkhead-associated

protein interaction domain, which is essential for activation

in response to DNA damage [104, 105]. When activated, the

CHEK2 protein inhibits CDC25C phosphatase, preventing

entry into mitosis, and also stabilizes the tumor suppressor

protein p53. Furthermore, the protein encoded by CHEK2

interacts with and phosphorylates BRCA1 [106].

CTLA4 is a member of the immunoglobulin super-

family, and this gene encodes a protein transmitting an

inhibitory signal to T cells. CTLA4 in humans maps to

chromosome 2q33 [107]. Schneider et al. concluded that

CTLA4 increases T-cell motility and overrides the T-cell

receptor-induced stop signal required for stable conjugate

formation between T cells and antigen-presenting cells

[108]. A series of SNPs were discovered by Ueda et al.

[109]. The polymorphisms in CTLA4 were tested by

Zhernakova et al. for association with type I diabetes [110].

The FGFR2 (fibroblast growth factor receptor 2) gene is

located on chromosome 10q26 and contains at least 22

exons [111]. FGFR2 is involved in the MAPK signaling

pathway, the endocytosis pathway, regulation of the actin

cytoskeleton pathway, pathways in cancer, and the prostate

cancer pathway [101]. The protein encoded by FGFR2 is a

member of the fibroblast growth factor receptor family,

which contributes to cell growth, invasiveness, motility,

and angiogenesis [112]. FGFR2 is over-expressed in both

breast cancer cell lines [113] and breast tumor tissues

[114]. Koziczak et al. identified the mechanism by which

FGFR promotes the proliferation of breast cancer cells

[115]. Moffa and Ethier concluded that aberrant expression

of FGFR2 in breast cancer cells results in sustained

C6orf97C6orf97FGFR2FGFR2FGFR2LSP1LSP1SLC4A7MRPS30RNF146TOX3TOX3ZNF365RAD51L1(CDKN2A,CDKN2B)(MAP3K1)(MAP3K1)(MYEOV,CCND1...)(ZMIZ1)(ANKRD16,FBXO18)IntergenicIntergenicIntergenicIntergenicIntergenic

Gene

rs2046210rs3757318rs2981579rs2981582rs1219648rs3817198rs909116rs4973768rs7716600rs2180341rs3803662rs4784227rs10995190rs999737rs1011970rs889312rs16886165rs614367rs704010rs2380205rs13281615rs11249433rs13387042rs981782rs1562430

Polymorphism

2009201020102007201020072010201020102008200720102010200920102010200920102010201020072009200720072010

Year

3,0278,5568,5567548,4287548,5568,5568,42854813,1454,1578,5562,2878,5568,5562,2878,5568,5568,5567542,28713,1457548,556

Subjects

1.29 (1.21, 1.37)1.30 (1.16, 1.46)1.43 (1.34, 1.53)1.26 (1.22, 1.30)1.32 (1.23, 1.42)1.07 (1.03, 1.11)1.17 (1.10, 1.24)1.16 (1.09, 1.24)1.24 (1.15, 1.34)1.41 (1.25, 1.59)1.28 (1.21, 1.35)1.24 (1.19, 1.29)1.16 (1.10, 1.22)1.06 (0.99, 1.14)1.09 (1.04, 1.14)1.22 (1.14, 1.30)1.23 (1.12, 1.35)1.15 (1.10, 1.20)1.07 (1.03, 1.11)1.06 (1.02, 1.10)1.08 (1.05, 1.11)1.16 (1.09, 1.24)1.20 (1.14, 1.26)1.04 (1.00, 1.08)1.17 (1.10, 1.25)

ratio (95% CI)Odds

1.2 1 1.5

Fig. 3 Risk of breast cancer

and genetic variants, as

evaluated in GWAS studies

Breast Cancer Res Treat (2011) 127:309–324 317

123

activation of signal transduction leading to transformation

[116].

HRAS was an additional variant identified in our review

as being noteworthy. HRAS is located on chromosome

11p15.5 and belongs to the Ras oncogene family. The

proteins encoded by these genes were reported to function

in signal transduction pathways. Defects in this gene are

implicated in many cancers, such as bladder cancer, fol-

licular thyroid cancer, and oral squamous cell carcinoma

[117, 118].

The IL1B (IL-1beta) protein, located at 5q11.2, is a

member of the interleukin 1 cytokine family [119]. This

cytokine is a pivotal mediator of the inflammatory

response, and it is involved in cell proliferation, differen-

tiation, and apoptosis. Microenvironmental IL1B and

IL1-alpha are required for the in vivo angiogenesis and

invasiveness of different tumor cells [120]. Furthermore,

antiangiogenic effects of IL1RN were reported, suggesting

a possible therapeutic role in cancer. Ben-Sasson et al.

[121] reported that IL1B signaling in T cells markedly

induces robust and durable primary and secondary CD4

responses.

LSP1 was mapped to 11p15.5 by May et al. [122] They

reported that LSP1 is an intracellular Ca2?- and F-actin

binding protein. Liu et al. [123] reported that LSP1

expressed in endothelium regulates neutrophil transendo-

thelial migration. Interestingly, the association between

rs3817198 (in LSP1) and breast cancer risk was noteworthy

in the genetic association studies but not noteworthy in the

GWAS. This finding may be due to the fact that to obtain

higher statistical power, the smaller the effect size is, the

bigger the sample size we required. With a small sample

size (390 cases and 364 controls) and a small OR: 1.07

(1.04–1.11) in the GWAS, the statistical power may be

small. Whereas with a bigger sample size (33,920 cases

and 35,671 controls) and a small OR: 1.06 (1.04–1.08) in

the genetic association studies, clearly, the statistical power

may be bigger than that in the GWAS. Thus, we think that

the noteworthy association between rs3817198 and breast

cancer is plausible.

MAP3K1, located at 5q11.2, is a serine/threonine

kinase. This kinase plays a key role in a network of

phosphorylating enzymes integrating cellular responses to

a number of mitogenic and metabolic stimuli [124]. Lu

et al. reported that MAP3K1 is an upstream activator of

ERK and JNK, and MAP3K1 is also an E3 ligase through

its PHD domain [125]. Roy et al. [126] demonstrated that

IFNG induces MEKK1 to up-regulate cellular responses.

C6orf97 is located at 6q25.1. The function of this gene

and its encoded protein is not known. Several GWASs have

suggested that the region around this gene is involved in

breast cancer [87, 88] and bone mineral density [127], but

no link to this specific gene has been found.

SLC4A7 is located on 3p22 [128]. Reiners et al. [129]

demonstrated that SLC4A7 and the USH2 (Usher syn-

drome 2A) proteins are partners in the supramolecular USH

protein network in the retina and inner ear. The visual and

auditory systems require H? disposal, which is mediated

by the sodium bicarbonate cotransporter NBC3 that is

encoded by the SLC4A7 gene [130].

MRPS30, located on 5p12-q11, is one of more than 70

protein components of mitochondrial ribosomes that are

encoded by the nuclear genome [131]. MRPS30 orthologs

were identified by Koc et al. in mouse, Drosophila, and

C. elegans but not in yeast or E. coli [132].

RNF146 is located on 6q22.1-q22.33 and contains 5

exons. It is up-regulated in the inferior temporal lobes of

Alzheimer disease patients compared with healthy controls

[133].

TOX3, located on 16q12.1 [90], belongs to the large and

diverse family of HMG-box proteins [134]. TOX3 is dif-

ferentially expressed in patients who experienced breast

cancer relapse to bone versus those patients who experi-

enced relapse elsewhere in the body [135]. Antoniou et al.

found that rs3803662 in TOX3 has multiplicative effects

on breast cancer risk in BRCA1 or BRCA2 mutation car-

riers [136].

The ZNF365 gene is located on 10q21.2 and contains 15

exons spanning approximately 300 kb. This gene has a

complex pattern of alternative splicing and transcriptional

start sites [137]. Wang et al. [138] found that ZNF365A

localizes to the centrosome throughout the cell cycle in

several human cell lines. A mutation analysis demonstrated

that the centrosomal localization of ZNF365A requires 2

coiled-coil subdomains but not the C-terminal zinc finger

structure. Over-expression of ZNF365A leads to abnormal

mitosis. A mutant form of ZNF365A lacking the C-termi-

nal region disruptes the localization of gamma-tubulin to

the centrosome.

As for the 99 variants with non-significant association

results, further studies are also needed to clarify the asso-

ciations with breast cancer risk, especially for those variants

with inconsistent results across different populations,

including AURKA (T91a), CYP1A1 (A2455G), ERCC2

(Asp312Asn), GSTP1 (Ile105Val), HSD17B1 (Ser312Gly),

MDM2 (SNP309), MTR (A2756G), and PGR (rs10895068).

We found 46 statistically significant associations

between gene variants and breast cancer, of which 10

(in eight genes) were considered noteworthy at a prior

probability of 0.001, and 99 statistically non-significant

gene-variant breast cancer associations. In addition, 25

statistically significant gene-variant breast cancer associa-

tions were reported in the GWASs, but only three SNPs

(rs2981582 and rs1219648 in FGFR2 and rs3817198 in

LSP1) were significantly associated with breast cancer risk

in both genetic association studies and GWASs, suggesting

318 Breast Cancer Res Treat (2011) 127:309–324

123

that the capacity of GWASs to detect disease susceptibility

genes is limited. To clarify the contribution of genetic

variants to breast cancer risk in different populations, fur-

ther studies are needed with large sample sizes and better

study design.

There are some limitations in this review, such as (1) the

associations were confined to those summarized in meta-

analyses, pooled analyses, and GWASs. Some individual

studies with larger sample sizes may have been missed, and

may possess more power to find significant associations

than some meta-analyses, pooled analyses, and GWASs;

(2) in the individual meta-analysis, the publication bias was

always addressed by the funnel plot and Egger’s tests. But

it is argued that the funnel plot can be misleading and

Egger’s test does not really test publication bias [139].

Actually, some studies with results of non-significant

associations tend to not be published. Furthermore, to

address the publication bias issue effectively in this review

was not realistic. Therefore, to some extent, the file drawer

problem may inevitably arise. In this case, even a small

number of studies lost ‘‘in the file drawer’’ can result in a

significant bias.

Undoubtedly, genetic association studies have been

helpful in the discovery of genetic susceptibility genes. In

particular, with the help of GWASs, many susceptibility

genes of several diseases have been confirmed. However,

several deficiencies of genetic association studies (or

GWASs) were observed, including their high cost and a

poor capacity to detect genetic susceptibility genes. Per-

haps the GWAS era will gradually wane, and with the

advance in technologies and price reductions, next-gener-

ation sequencing can open a new era in the discovery of

genetic susceptibility disease genes [140–144].

In summary, in this review of genetic association stud-

ies, 31.7% of the gene-variant breast cancer associations

were significant, and 21.7% of these significant associa-

tions were noteworthy. However, in GWASs, 80% of the

significant associations were noteworthy.

Acknowledgments This study was supported by The ‘‘Eleventh

Five-Year’’ Science and Technology Support Plan of the Ministry of

Science and Technology of China (MSTC, 2009BA180B00) and a

grant from the Natural Science Foundation of Zhejiang Province

(NSFZJ, Y2090081). We thank Dr. Iain Bruce for valuable comments

and English editing.

Conflict of interest None.

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