Null genotype of GSTT1 contributes to increased Parkinson’s disease risk in Caucasians: evidence from a meta-analysis

Conflicting results in previous case–control studies on the association between Glutathione S-transferase T1 (GSTT1) gene polymorphism and Parkinson’s disease (PD) risk have been reported, so we conducted this meta-analysis. We searched and extracted data from 3 Chinese and 3 English web-based electronic databases to evaluate the associations by odds ratio (OR) and its 95 % confidence interval (CI) under the recessive genetic comparison model (null genotype vs. present genotype). We also conducted subgroup analyses by ethnicity and adjusted status of OR, respectively. Meta-analyses and subgroup analyses of larger studies (sample size ≥300) were also reanalyzed. When 18 eligible studies (3,963 PD cases and 5,472 controls) were pooled to analyze the association, we found no statistically significant result (OR 1.24, 95 % CI 0.96–1.60). In the subgroup analyses by ethnicity, there was statistically significant association between the null genotype of GSTT1 and PD risk among Caucasians, while the associations were not found among Asians and Latinos. In the subgroup analyses by adjusted status of OR, there were no significant associations both in studies with crude OR and adjusted OR. Meta-analyses and subgroup analyses of larger studies (sample size ≥300) were also confirmed the associations mentioned above. Power analysis indicated only meta-analysis of Caucasians had enough evidence to claim the association. In conclusion, the meta-analysis suggests that the null genotype of GSTT1 contributes to PD risk in Caucasians, and no association in Asians is needed more studies to confirm.     

Association mapping of disease loci, by use of a pooled DNA genomic screen.

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification. These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.     

Combined analysis from eleven linkage studies of bipolar disorder provides strong evidence of susceptibility loci on chromosomes 6q and 8q

Several independent studies and meta-analyses aimed at identifying genomic regions linked to bipolar disorder (BP) have failed to find clear and consistent evidence of linkage regions. Our hypothesis is that combining the original genotype data provides benefits of increased power and control over sources of heterogeneity that outweigh the difficulty and potential pitfalls of the implementation. We conducted a combined analysis using the original genotype data from 11 BP genomewide linkage scans comprising 5,179 individuals from 1,067 families. Heterogeneity among studies was minimized in our analyses by using uniform methods of analysis and a common, standardized marker map and was assessed using novel methods developed for meta-analysis of genome scans. To date, this collaboration is the largest and most comprehensive analysis of linkage samples involving a psychiatric disorder. We demonstrate that combining original genome-scan data is a powerful approach for the elucidation of linkage regions underlying complex disease. Our results establish genomewide significant linkage to BP on chromosomes 6q and 8q, which provides solid information to guide future gene-finding efforts that rely on fine-mapping and association approaches.     

Meta-analysis of epidemiological studies demonstrates significant association of PTGS2 polymorphism rs689470 and no significant association of rs20417 with prostate cancer

Evidence is accumulating that chronic inflammation has an important role in prostate cancer. Two common polymorphisms in the prostaglandin-endoperoxide synthase 2 (PTGS2) gene, rs20417 and rs689470, have been found to alter the risk for prostate cancer, but the various studies are not in agreement. To derive a more precise estimation of this association, all available studies were considered in a meta-analysis, with 10,700 patients and 13,021 controls for rs20417 and 4087 patients and 3761 controls for rs689470. We used odds ratios (ORs) to assess the strength of the association, and 95% confidence intervals (CIs) to determine the precision of the estimate. When all groups were pooled, we did not detect a significant association of rs20417 polymorphism with prostate cancer risk. Similarly, no associations were found in the subgroup analysis. However, we found that rs689470 was significantly associated with a trend towards increased prostate cancer risk when using both additive (OR = 2.15, 95%CI = 1.04-4.44, P = 0.04) and recessive models (OR = 2.07, 95%CI = 1.07-4.03, P = 0.03) to analyze the data. In subgroup analyses stratified by ethnicity, there was no evidence that rs689470 has a significant association with prostate cancer in Caucasians. Based on our meta-analysis, rs689470 polymorphism is significantly associated with prostate cancer risk in the overall population. Nevertheless, we suggest that further studies should be made to confirm these findings.     

The association between common genetic variant of microRNA-146a and cancer susceptibility

Published data on the association between microRNA-146a (miR-146a) G/C polymorphism and cancer susceptibility are inconclusive. To derive a more precise estimation of the relationship, a meta-analysis was performed. A total of 23 studies including 10,585 cases and 12,183 controls were used in the meta-analysis. Overall, no significant associations were found between miR-146a G/C polymorphism and cancer risk when all studies pooled into the meta-analysis (GC vs. CC: OR=1.08, 95% CI=0.94-1.24; GG vs. CC: OR=1.13, 95% CI=0.93-1.37; dominant model: OR=1.09, 95% CI=0.94-1.26). In the subgroup analysis by ethnicity, still no significant associations were found. In the subgroup analysis by cancer type, statistically significantly increased risks were found for papillary thyroid carcinoma (GC vs. CC: OR=3.44, 95% CI=1.86-6.34; GG vs. CC: OR=2.20, 95% CI=1.22-3.99; dominant model: OR=2.68, 95% CI=1.48-4.83). In the subgroup analysis by population-based controls or hospital-based controls, no statistically significantly increased risks were found. Despite some limitations, this meta-analysis suggests that the miR-146a G allele is a low-penetrant risk factor for papillary thyroid carcinoma development.     

A comparison in association and linkage genome-wide scans for alcoholism susceptibility genes using single-nucleotide polymorphisms

We conducted genome-wide linkage scans using both microsatellite and single-nucleotide polymorphism (SNP) markers. Regions showing the strongest evidence of linkage to alcoholism susceptibility genes were identified. Haplotype analyses using a sliding-window approach for SNPs in these regions were performed. In addition, we performed a genome-wide association scan using SNP data. SNPs in these regions with evidence of association (P 相似文献   

Lack of association between methylenetetrahydrofolate reductase gene A1298C polymorphism and breast cancer susceptibility

Published data on the association between methylenetetrahydrofolate reductase gene (MTHFR) A1298C polymorphism and breast cancer risk are inconclusive. To derive a more precise estimation of the relationship, a meta-analysis was performed. Medline, PubMed, Embase, and Web of Science were searched. Crude ORs with 95% CIs were used to assess the strength of association between the MTHFR A1298C polymorphism and breast cancer risk. The pooled ORs were performed for co-dominant model (AC vs. AA, CC vs. AA), dominant model (CC+AC vs. AA), and recessive model (CC vs. AC+AA), respectively. A total of 26 studies including 12,244 cases and 15,873 controls were involved in this meta-analysis. Overall, no significant associations were found between MTHFR A1298C polymorphism and breast cancer risk when all studies pooled into the meta-analysis (AC vs. AA: OR=0.99, 95% CI 0.94-1.05; CC vs. AA: OR 0.99, 95% CI 0.90-1.09; dominant model: OR=0.99, 95% CI 0.95-1.04; and recessive model: OR=0.98, 95% CI 0.90-1.08). In the subgroup analysis by ethnicity or study design, still no significant associations were found for all comparison models. In conclusion, this meta-analysis suggests that the MTHFR A1298C polymorphism may be not associated with breast cancer development. However, large sample and representative population-based studies with homogeneous breast cancer patients and well matched controls are warranted to confirm this finding.     

Absence of linkage of phonological coding dyslexia to chromosome 6p23-p21.3 in a large family data set.

Previous studies have suggested that a locus predisposing to specific reading disability (dyslexia) resides on chromosome 6p23-p21.3. We investigated 79 families having at least two siblings affected with phonological coding dyslexia, the most common form of reading disability (617 people genotyped, 294 affected), and we tested for linkage with the genetic markers reported to be linked to dyslexia in those studies. No evidence for linkage was found by LOD score analysis or affected-sib-pair methods. However, using the affected-pedigree-member (APM) method, we detected significant evidence for linkage and/or association with some markers when we used published allele frequencies with weighting of rarer alleles. APM results were not significant when we used marker allele frequencies estimated from parents. Furthermore, results were not significant with the more robust SIMIBD method using either published or parental marker frequencies. Finally, family-based association analysis using the AFBAC program showed no evidence for association with any marker. We conclude that the APM method should be used only with extreme caution, because it appears to have generated false-positive results. In summary, using a large data set with high power to detect linkage, we were unable to find evidence for linkage or association between phonological coding dyslexia and chromosome 6p markers.     

Genetic association analysis using sibship data: a multilevel model approach

Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.     

Impact of the diagnosis definition on linkage detection

Previous genome scan linkage analyses of the disease Kofendrerd Personality Disorder (KPD) with microsatellites led to detect some regions on chromosomes 1, 3, 5, and 9 that were identical for the three populations AI, KA, and DA but with large differences in significance levels. These differences in results may be explained by the different diagnosis definitions depending on the presence/absence of 12 traits that were used in the 3 populations AI, KA, and DA. Heterogeneity of linkage was thus investigated here according to the absence/presence of each of the 12 traits in the 3 populations. For this purpose, two methods, the triangle test statistic and the predivided sample test were applied to search for genetic heterogeneity. Three regions with a strong heterogeneity of linkage were detected: the region on chromosome 1 according to the presence/absence of the traits a and b, the region on chromosome 3 for the trait b, and the region on chromosome 9 for the traits k and l. These 3 regions were the same as those detected by linkage analyses. No novel region was detected by the heterogeneity tests. Concerning chromosome 1, linkage analyses showed a much stronger evidence of linkage for traits a and b and for a combination of these traits than for KPD. Moreover, there was no indication of linkage to any of the other traits used to define the diagnosis of KPD. A genetic factor located on the chromosome 1 may have been detected here which would be involved specifically in traits a and b or in a combination of these traits.     

Association between KCNJ11 gene polymorphisms and risk of type 2 diabetes mellitus in East Asian populations: a meta-analysis in 42,573 individuals

A number of studies have been performed to identify the association between potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) gene and type 2 diabetes mellitus (T2DM) in East Asian populations, with inconsistent results. The main aim of this work was to evaluate more precisely the genetic influence of KCNJ11 on T2DM in East Asian populations by means of a meta-analysis. We identified 20 articles for qualitative analysis and 16 were eligible for quantitative analysis (meta-analysis) by database searching up to May 2010. The association was assessed under different genetic models, and the pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. The allelic and genotypic contrast demonstrated that the association between KCNJ11 and T2DM was significant for rs5210. However, not all results for rs5215 and rs5218 showed significant associations. For rs5219, the combined ORs (95% CIs) for allelic contrast, dominant and recessive models contrast (with allelic frequency and genotypic distribution data) were 1.139 (1.093–1.188), 1.177 (1.099–1.259) and 1.207 (1.094–1.332), respectively (random effect model). The analysis on the most completely adjusted ORs (95% CIs) by the covariates of rs5219 all presented significant associations under different genetic models. Population-stratified analysis (Korean, Japanese and Chinese) and sensitivity analysis verified the significant results. Cumulative meta-analysis including publication time and sample size illustrated the exaggerated genetic effect in the earliest studies. Heterogeneity and publication bias were assessed. Our study verified that single nucleotide polymorphisms (SNPs) of KCNJ11 gene were significantly associated with the risk of T2DM in East Asian populations.     

Validation of whole genome linkage‐linkage disequilibrium and association results,and identification of markers to predict genetic merit for twinning

A previous genome‐wide search with a moderate density 10K marker set identified many marker associations with twinning rate, either through single‐marker analysis or combined linkage‐linkage disequilibrium (LLD; haplotype) analysis. The objective of the current study was to validate putative marker associations using an independent set of phenotypic data. Holstein bulls (n = 921) from 100 paternal half‐sib families were genotyped. Twinning rate predicted transmitting abilities were calculated using calving records from 1994 to 1998 (Data I) and 1999 to 2006 (Data II), and the underlying liability scores from threshold model analysis were used as the trait in marker association analyses. The previous analysis used 201 bulls with daughter records in Data I. In the current analysis, this was increased to 434, providing a revised estimate of effect and significance. Bulls with daughter records in Data II totaled 851, and analysis of this data provided the validation of results from analysis of Data I. Single nucleotide polymorphisms (SNPs) were selected to validate previously significant single‐marker associations and LLD results. Bulls were genotyped for a total of 306 markers. Nine of 13 LLD regions located on chromosomes 1, 2, 3, 6, 9, 22, 23(2) and 26 were validated, showing significant results for both Data I and II. Association analysis revealed 55 of 174 markers validated, equating to a single‐marker validation rate of 31%. Stepwise backward elimination and cross‐validation analyses identified 18 SNPs for use in a final reduced marker panel explaining 34% of the genetic variation, and to allow prediction of genetic merit for twinning rate.     

Association of serum gamma-glutamyltransferase with chronic kidney disease risk: a meta-analysis

Studies on the association between gamma-glutamyltransferase (GGT) and chronic kidney disease (CKD) have produced conflicting results. This study aimed to investigate the association of elevated GGT with the risk of CKD in the adult general population by performing a meta-analysis of relevant articles. We searched PubMed, Web of Science, and Embase databases from their inception to October 2017 to identify observational studies regarding the association between serum GGT level and CKD. Pooled risk ratio (RR) and 95% confidence intervals (CI) was calculated as the effective measures. Eight studies with 116 011 individuals were included in this meta-analysis. Overall, individuals with the highest versus the lowest GGT level were not associated with an increased risk of CKD (RR 1.14; 95% CI 0.99–1.31). Subgroup analysis showed that the pooled RR for the highest versus the lowest GGT category was 1.31 (95% CI 1.06–1.60) for the Asian countries and 1.04 (95% CI 0.88–1.23) for the Western countries. In the sex-stratified analysis, no significant associations of elevated GGT with risk of CKD were found for both women (RR 0.95; 95% CI 0.84–1.07) and men (RR 1.08; 95% CI 0.86–1.36). This meta-analysis demonstrates no significant association between elevated serum GGT and risk of CKD in the adult general population. However, future well-designed studies are needed to evaluate the impact of geographical region on the association.     

Association between miR-146a rs2910164 polymorphism and autoimmune diseases susceptibility: A meta-analysis

Published data on the rs2910164 in microRNA-146a (miR-146a) are shown to be associated with increased or decreased autoimmune diseases risk. To derive a more precise estimation of the relationship, we performed a meta-analysis to systematically summarize the possible. A meta-analysis including 11 studies with 3042 controls and 2197 cases was performed for genotypes CC (recessive effect), CC + CG (dominant effect) and C allele in fixed or random-effects models based on between-study heterogeneity. Overall, no significant association between miR-146a G/C rs2910164 polymorphism and autoimmune diseases risk was found in all genetic models when all studies were pooled into the meta-analysis. SLE (OR = 0.99, 95% CI: 0.90–1.10), RA (OR = 0.98, 95% CI: 0.85–1.14) did not yield statistical significance as for C allele pooled studies. In the subgroup analysis by ethnicity, still no significant association was detected in all genetic models. Our meta-analysis suggests that there is no association between miR-146a G/C rs2910164 polymorphism and the development of autoimmune diseases.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

Phylogenetic analysis and comparative data: a test and review of evidence

The question is often raised whether it is statistically necessary to control for phylogenetic associations in comparative studies. To investigate this question, we explore the use of a measure of phylogenetic correlation, lambda, introduced by Pagel (1999), that normally varies between 0 (phylogenetic independence) and 1 (species' traits covary in direct proportion to their shared evolutionary history). Simulations show lambda to be a statistically powerful index for measuring whether data exhibit phylogenetic dependence or not and whether it has low rates of Type I error. Moreover, lambda is robust to incomplete phylogenetic information, which demonstrates that even partial information on phylogeny will improve the accuracy of phylogenetic analyses. To assess whether traits generally show phylogenetic associations, we present a quantitative review of 26 published phylogenetic comparative data sets. The data sets include 103 traits and were chosen from the ecological literature in which debate about the need for phylogenetic correction has been most acute. Eighty-eight percent of data sets contained at least one character that displayed significant phylogenetic dependence, and 60% of characters overall (pooled across studies) showed significant evidence of phylogenetic association. In 16% of tests, phylogenetic correlation could be neither supported nor rejected. However, most of these equivocal results were found in small phylogenies and probably reflect a lack of power. We suggest that the parameter lambda be routinely estimated when analyzing comparative data, since it can also be used simultaneously to adjust the phylogenetic correction in a manner that is optimal for the data set, and we present an example of how this may be done.     

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6-7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160-167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287-294 cM, including D2), chromosome 5 (p-value < 0.001 for 0-7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7-14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.     

Genetic association analysis using data from triads and unrelated subjects

The selection of an appropriate control sample for use in association mapping requires serious deliberation. Unrelated controls are generally easy to collect, but the resulting analyses are susceptible to spurious association arising from population stratification. Parental controls are popular, since triads comprising a case and two parents can be used in analyses that are robust to this stratification. However, parental controls are often expensive and difficult to collect. In some situations, studies may have both parental and unrelated controls available for analysis. For example, a candidate-gene study may analyze triads but may have an additional sample of unrelated controls for examination of background linkage disequilibrium in genomic regions. Also, studies may collect a sample of triads to confirm results initially found using a traditional case-control study. Initial association studies also may collect each type of control, to provide insurance against the weaknesses of the other type. In these situations, resulting samples will consist of some triads, some unrelated controls, and, possibly, some unrelated cases. Rather than analyze the triads and unrelated subjects separately, we present a likelihood-based approach for combining their information in a single combined association analysis. Our approach allows for joint analysis of data from both triad and case-control study designs. Simulations indicate that our proposed approach is more powerful than association tests that are based on each separate sample. Our approach also allows for flexible modeling and estimation of allele effects, as well as for missing parental data. We illustrate the usefulness of our approach using SNP data from a candidate-gene study of psoriasis.     

Using linkage genome scans to improve power of association in genome scans

Scanning the genome for association between markers and complex diseases typically requires testing hundreds of thousands of genetic polymorphisms. Testing such a large number of hypotheses exacerbates the trade-off between power to detect meaningful associations and the chance of making false discoveries. Even before the full genome is scanned, investigators often favor certain regions on the basis of the results of prior investigations, such as previous linkage scans. The remaining regions of the genome are investigated simultaneously because genotyping is relatively inexpensive compared with the cost of recruiting participants for a genetic study and because prior evidence is rarely sufficient to rule out these regions as harboring genes with variation of conferring liability (liability genes). However, the multiple testing inherent in broad genomic searches diminishes power to detect association, even for genes falling in regions of the genome favored a priori. Multiple testing problems of this nature are well suited for application of the false-discovery rate (FDR) principle, which can improve power. To enhance power further, a new FDR approach is proposed that involves weighting the hypotheses on the basis of prior data. We present a method for using linkage data to weight the association P values. Our investigations reveal that if the linkage study is informative, the procedure improves power considerably. Remarkably, the loss in power is small, even when the linkage study is uninformative. For a class of genetic models, we calculate the sample size required to obtain useful prior information from a linkage study. This inquiry reveals that, among genetic models that are seemingly equal in genetic information, some are much more promising than others for this mode of analysis.     

Association between SHBG Asp327Asn (rs6259) polymorphism and breast cancer risk: a meta-analysis of 10,454 cases and 13,111 controls

Sex hormone-binding globulin (SHBG) is a plasma glycoprotein that plays an important role in breast cancer pathophysiology and risk definition, since it regulates the bioavailable fraction of circulating estradiol. Epidemiological studies have evaluated the association between SHBG Asp327Asn polymorphism and breast cancer risk in diverse populations. However, the results remain conflicting rather than conclusive. This meta-analysis of literatures was performed to derive a more precise estimation of the relationship. A total of 10 studies were identified for the meta-analysis, including 10,454 cases and 13,111 controls for SHBG Asp327Asn polymorphism. When all studies were pooled into the meta-analysis, there was no evidence for significant association between SHBG Asp327Asn polymorphism and breast cancer risk (for Asn/Asn vs. Asp/Asp: OR = 1.20, 95 % CI = 0.94-1.55; for Asp/Asn vs. Asp/Asp: OR = 0.94, 95 % CI = 0.87-1.01; for dominant model: OR = 0.95, 95 % CI = 0.90-1.02; for recessive model: OR = 1.22, 95 % CI = 0.95-1.57). In the subgroup analyses by ethnicity, menopausal status, and source of controls, no significant associations were found in all genetic models. Interestingly, further analyses stratified by menopausal status in different ethnicities revealed that this polymorphism might provide protective effects against breast cancer risk in postmenopausal Asian women (for dominant model: OR = 0.83, 95 % CI = 0.70-0.97). Sensitivity analyses were performed by sequential removal of individual studies and cumulative statistics have showed combined ORs were not materially altered by any individual study under all comparisons. In summary, this meta-analysis suggests that SHBG Asp327Asn polymorphism is not associated with breast cancer risk overall, while it might be an important genetic susceptibility factor in postmenopausal Asian women for developing breast cancer. Larger and well-designed studies are warranted to confirm our findings in the future.