Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS

In spite of the tremendous success of genome-wide association studies (GWAS) in identifying genetic variants associated with complex traits and common diseases, many more are yet to be discovered. Hence, it is always desirable to improve the statistical power of GWAS. Paralleling with the intensive efforts of integrating GWAS with functional annotations or other omic data, we propose leveraging other published GWAS summary data to boost statistical power for a new/focus GWAS; the traits of the published GWAS may or may not be genetically correlated with the target trait of the new GWAS. Building on weighted hypothesis testing with a solid theoretical foundation, we develop a novel and effective method to construct single-nucleotide polymorphism (SNP)-specific weights based on 22 published GWAS data sets with various traits, detecting sometimes dramatically increased numbers of significant SNPs and independent loci as compared to the standard/unweighted analysis. For example, by integrating a schizophrenia GWAS summary data set with 19 other GWAS summary data sets of nonschizophrenia traits, our new method identified 1,585 genome-wide significant SNPs mapping to 15 linkage disequilibrium-independent loci, largely exceeding 818 significant SNPs in 13 independent loci identified by the standard/unweighted analysis; furthermore, using a later and larger schizophrenia GWAS summary data set as the validation data, 1,423 (out of 1,585) significant SNPs identified by the weighted analysis, compared to 705 (out of 818) by the unweighted analysis, were confirmed, while all 15 and 13 independent loci were also confirmed. Similar conclusions were reached with lipids and Alzheimer's disease (AD) traits. We conclude that the proposed approach is simple and cost-effective to improve GWAS power.     

乳腺癌全基因组关联研究的现状及其应用价值的分析方法

目的 从全基因组关联研究（GWAS）发现的与2型糖尿病（T2DM）发病风险相关的单核苷酸基因多态性（SNP）位点中,筛选出可用于预测妊娠期糖尿病（GDM）女性产后出现T2DM的SNP。 方法 利用2009 — 2010年天津市妊娠期糖尿病随机干预试验研究基线收集的1 240名GDM女性（80名T2DM和1 160名非T2DM）血样,检测并分析40个GWAS发现的与T2DM发病相关的SNP位点。基于单个SNP的遗传风险解释比例,结合产后T2DM的发生情况,联合采用ROC曲线下面积（AUC）、整合区分指数（IDI）及再分类净优化指数（NRI）进行候选SNP筛选。 结果 以遗传风险解释比例最大的SNP（rs10906115）为基础,依次纳入其他的SNP,共发现1个SNP可显著改善AUC（P = 0.019）,8个SNP可显著改善IDI（P < 0.05）,8个SNP可显著改善NRI（P < 0.05）。综合所有40个GWAS发现的SNP位点对AUC、IDI及NRI的改善情况,共有11个SNP（rs10906115,rs2779116,rs7034200,rs7041847,rs780094,rs5015480,rs11212617,rs831571,rs7944584,rs6815464,rs35767）可用于预测GDM女性产后发生T2DM的风险,同时11个SNP可累计解释5.5 %的GDM女性产后发生T2DM的遗传风险。 结论 为避免资源浪费,建议在众多GWAS发现的与T2DM发病相关的SNP位点中,选择能够显著改善风险预测能力或风险再分类能力的11个SNP用于预测GDM女性产后发生T2DM的风险。     

Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies

Genome-wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of ancestry-informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from genome-wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a genome-wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of genome-wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a genomewide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use.     

Association of obesity-related genetic variants with endometrial cancer risk: a report from the Shanghai Endometrial Cancer Genetics Study

Obesity is a well-established risk factor for endometrial cancer, the most common gynecologic malignancy. Recent genome-wide association studies (GWAS) have identified multiple genetic markers for obesity. The authors evaluated the association of obesity-related single nucleotide polymorphisms (SNPs) with endometrial cancer using GWAS data from their recently completed study, the Shanghai Endometrial Cancer Genetics Study, which comprised 832 endometrial cancer cases and 2,049 controls (1996-2005). Thirty-five SNPs previously associated with obesity or body mass index (BMI; weight (kg)/height (m)(2)) at a minimum significance level of ≤5 × 10(-7) in the US National Human Genome Research Institute's GWAS catalog (http://genome.gov/gwastudies) and representing 26 unique loci were evaluated by either direct genotyping or imputation. The authors found that for 22 of the 26 unique loci tested (84.6%), the BMI-associated risk variants were present at a higher frequency in cases than in population controls (P = 0.0003). Multiple regression analysis showed that 9 of 35 BMI-associated variants, representing 7 loci, were significantly associated (P ≤ 0.05) with the risk of endometrial cancer; for all but 1 SNP, the direction of association was consistent with that found for BMI. For consistent SNPs, the allelic odds ratios ranged from 1.15 to 1.29. These 7 loci are in the SEC16B/RASAL, TMEM18, MSRA, SOX6, MTCH2, FTO, and MC4R genes. The associations persisted after adjustment for BMI, suggesting that genetic markers of obesity provide value in addition to BMI in predicting endometrial cancer risk.     

Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS

Genome‐wide association studies (GWAS) have confirmed the ubiquitous existence of genetic heterogeneity for common disease: multiple common genetic variants have been identified to be associated, while many more are yet expected to be uncovered. However, the single SNP (single‐nucleotide polymorphism) based trend test (or its variants) that has been dominantly used in GWAS is based on contrasting the allele frequency difference between the case and control groups, completely ignoring possible genetic heterogeneity. In spite of the widely accepted notion of genetic heterogeneity, we are not aware of any previous attempt to apply genetic heterogeneity motivated methods in GWAS. Here, to explicitly account for unknown genetic heterogeneity, we applied a mixture model based single‐SNP test to the Wellcome Trust Case Control Consortium (WTCCC) GWAS data with traits of Crohn's disease, bipolar disease, coronary artery disease, and type 2 diabetes, identifying much larger numbers of significant SNPs and risk loci for each trait than those of the popular trend test, demonstrating potential power gain of the mixture model based test.     

2型糖尿病基因多态性的病例对照研究

[目的]研究2型糖尿病与脂联素基因SNP+45、SNP+276的相互关系。[方法]采用单因素非条件Logistic回归分析,对上海市浦东新区常住居民中新发糖尿病患者(糖尿病组)及社区正常人群(对照组)各590人进行病例-对照研究。[结果]糖尿病组与对照组比较,脂联素基因SNP+45的3种基因型和等位基因分布的差异无统计学意义(χ2=1.44,P>0.05;χ2=1.35,P>0.05)。脂联素基因SNP+276的3种基因型和等位基因分布的差异有统计学意义(χ2=8.45,P<0.05;χ2=8.99,P<0.05),糖尿病患者T/T基因型多于对照组人群。单因素分析显示,脂联素基因SNP+276与糖尿病的关系有统计学意义。[结论]脂联素基因SNP+276与浦东新区汉族人群2型糖尿病的发病相关,脂联素基因的变异可能在2型糖尿病发病过程中起重要作用。     

SNP Set Association Analysis for Familial Data

Genome‐wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single‐nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine‐based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual‐SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score‐based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within‐family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.     

Sifting the wheat from the chaff: prioritizing GWAS results by identifying consistency across analytical methods

The curse of multiple testing has led to the adoption of a stringent Bonferroni threshold for declaring genome-wide statistical significance for any one SNP as standard practice. Although justified in avoiding false positives, this conservative approach has the potential to miss true associations as most studies are drastically underpowered. As an alternative to increasing sample size, we compare results from a typical SNP-by-SNP analysis with three other methods that incorporate regional information in order to boost or dampen an otherwise noisy signal: the haplotype method (Schaid et al. [2002] Am J Hum Genet 70:425-434), the gene-based method (Liu et al. [2010] Am J Hum Genet 87:139-145), and a new method (interaction count) that uses genome-wide screening of pairwise SNP interactions. Using a modestly sized case-control study, we conduct a genome-wide association studies (GWAS) of age-related macular degeneration, and find striking agreement across all methods in regions of known associated variants. We also find strong evidence of novel associated variants in two regions (Chromosome 2p25 and Chromosome 10p15) in which the individual SNP P-values are only suggestive, but where there are very high levels of agreement between all methods. We propose that consistency between different analysis methods may be an alternative to increasingly larger sample sizes in sifting true signals from noise in GWAS.     

Genome-wide association study of antibody response to smallpox vaccine

We performed a genome-wide association study (GWAS) of antibody levels in a multi-ethnic group of 1071 healthy smallpox vaccine recipients. In Caucasians, the most prominent association was found with promoter SNP rs10489759 in the LOC647132 pseudogene on chromosome 1 (p=7.77×10(-8)). In African-Americans, we identified eight genetic loci at p<5×10(-7). The SNP association with the lowest p-value (rs10508727, p=1.05×10(-10)) was in the Mohawk homeobox (MKX) gene on chromosome 10. Other candidate genes included LOC388460, GPR158, ZHX2, SPIRE1, GREM2, CSMD1, and RUNX1. In Hispanics, the top six associations between genetic variants and antibody levels had p-values less than 5×10(-7), with p=1.78×10(-10) for the strongest statistical association (promoter SNP rs12256830 in the PCDH15 gene). In addition, SNP rs4748153 in the immune response gene PRKCQ (protein kinase C, theta) was significantly associated with neutralizing antibody levels (p=2.51×10(-8)). Additional SNP associations in Hispanics (p≤3.40×10(-7)) were mapped to the KIF6/LOC100131899, CYP2C9, and ANKLE2/GOLGA3 genes. This study has identified candidate SNPs that may be important in regulating humoral immunity to smallpox vaccination. Replication studies, as well as studies elucidating the functional consequences of contributing genes and polymorphisms, are underway.     

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.     

The impact of newly identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts

Recently, genome wide association studies (GWAS) have identified a number of single nucleotide polymorphisms (SNPs) as being associated with coronary heart disease (CHD). We estimated the effect of these SNPs on incident CHD, stroke and total mortality in the prospective cohorts of the MORGAM Project. We studied cohorts from Finland, Sweden, France and Northern Ireland (total N=33,282, including 1,436 incident CHD events and 571 incident stroke events). The lead SNPs at seven loci identified thus far and additional SNPs (in total 42) were genotyped using a case-cohort design. We estimated the effect of the SNPs on disease history at baseline, disease events during follow-up and classic risk factors. Multiple testing was taken into account using false discovery rate (FDR) analysis. SNP rs1333049 on chromosome 9p21.3 was associated with both CHD and stroke (HR=1.20, 95% CI 1.08-1.34 for incident CHD events and 1.15, 0.99-1.34 for incident stroke). SNP rs11670734 (19q12) was associated with total mortality and stroke. SNP rs2146807 (10q11.21) showed some association with the fatality of acute coronary event. SNP rs2943634 (2q36.3) was associated with high density lipoprotein (HDL) cholesterol and SNPs rs599839, rs4970834 (1p13.3) and rs17228212 (15q22.23) were associated with non-HDL cholesterol. SNPs rs2943634 (2q36.3) and rs12525353 (6q25.1) were associated with blood pressure. These findings underline the need for replication studies in prospective settings and confirm the candidacy of several SNPs that may play a role in the etiology of cardiovascular disease.     

Genetische Ursachen der Adipositas

A small number of confirmed major genes for human obesity has been identified by molecular genetic studies; mutations of these have a strong influence on the development of excessive body weight. However, the underlying mutations are rare and do not explain the current obesity epidemic. The genetic predisposition to common obesity most likely has a polygenic basis, and each single gene variant has only a small influence on body weight. The introduction of genome-wide association scans (GWAS) offers new opportunities for the study of complex diseases. The receptor variant with the amino acid isoleucin (wildtype: valine) at position 103 of the melanocortin-4 receptor (MC4R) represents the first confirmed polygenic variant with an influence on body mass index; additional polymorphisms located 188 kb at the 3’ end of the MC4R have also been shown to have an effect on body weight. Variants in the first intron of the “fat mass and obesity associated” gene (FTO) confer the most pronounced polygenic effect on obesity (+0.4 kg/m² per allele); these variants were originally detected in 2007 in GWAS pertaining to type 2 diabetes mellitus. Recently, additional SNPs with a polygenic effect on obesity have been identified in three large GWAS. By December 2009, 17 solidly confirmed polygenes for body weight regulation have been reported.     

Genotype Imputation of MetabochipSNPs Using a Study-Specific Reference Panel of ∼4,000 Haplotypes in African Americans From the Women's Health Initiative

Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2) (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2) of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2) for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.     

Circulating Adiponectin and Its Association with Metabolic Traits and Type 2 Diabetes: Gene-Diet Interactions Focusing on Selected Gene Variants and at the Genome-Wide Level in High-Cardiovascular Risk Mediterranean Subjects

Adiponectin is gaining renewed interest since, in addition to its possible protective role against insulin resistance and arteriosclerosis, recent studies suggest other additional favorable effects. However, the influence of gene-diet interactions on plasma adiponectin levels is still little understood. We analyzed the association between plasma adiponectin levels and various metabolic traits in a high-cardiovascular risk Mediterranean population, as well as the genetic effect of four candidate single-nucleotide polymorphisms (SNPs) in the adiponectin gene (ADIPOQ) and their interactions with the Mediterranean dietary pattern. Additionally, we explored, at the genome-wide level, the SNPs most associated with plasma adiponectin levels, as well as gene–diet interactions with the Mediterranean diet. In the 954 participants studied (aged 55–80 years), plasma adiponectin levels were strongly associated with plasma HDL-C concentrations (p = 6.6 × 10⁻³⁶) and inversely related to triglycerides (p = 4.7 × 10⁻¹⁸), fasting glucose (p = 3.5 × 10⁻¹⁶) and type 2 diabetes (p = 1.4 × 10⁻⁷). Of the four pre-selected ADIPOQ candidate SNPs, the one most associated with plasma adiponectin was the −11391G > A (rs17300539) promoter SNP (p = 7.2 × 10⁻⁵, in the multivariable adjusted model). No significant interactions with the Mediterranean diet pattern were observed for these SNPs. Additionally, in the exploratory genome-wide association study (GWAS), we found new SNPs associated with adiponectin concentrations at the suggestive genome-wide level (p < 1 × 10⁻⁵) for the whole population, including the lead SNP rs9738548 (intergenic) and rs11647294 in the VAT1L (Vesicle Amine Transport 1 Like) gene. We also found other promising SNPs on exploring different strata such as men, women, diabetics and non-diabetics (p = 3.5 × 10⁻⁸ for rs2850066). Similarly, we explored gene–Mediterranean diet interactions at the GWAS level and identified several SNPs with gene–diet interactions at p < 1 × 10⁻⁵. A remarkable gene–diet interaction was revealed for the rs2917570 SNP in the OPCML (Opioid Binding Protein/Cell Adhesion Molecule Like) gene, previously reported to be associated with adiponectin levels in some populations. Our results suggest that, in this high-cardiovascular risk Mediterranean population, and even though adiponectin is favorably associated with metabolic traits and lower type 2 diabetes, the gene variants more associated with adiponectin may be population-specific, and some suggestive gene–Mediterranean diet interactions were detected.     

Fasting glucose GWAS candidate region analysis across ethnic groups in the Multiethnic Study of Atherosclerosis (MESA)

Genetic variants associated with fasting glucose in European ancestry populations are increasingly well understood. However, the nature of the associations between these single nucleotide polymorphisms (SNPs) and fasting glucose in other racial and ethnic groups is unclear. We sought to examine regions previously identified to be associated with fasting glucose in Caucasian genome-wide association studies (GWAS) across multiple ethnicities in the Multiethnic Study of Atherosclerosis (MESA). Nondiabetic MESA participants with fasting glucose measured at the baseline exam and with GWAS genotyping were included; 2,349 Caucasians, 664 individuals of Chinese descent, 1,366 African Americans, and 1,171 Hispanics. Genotype data were generated from the Affymetrix 6.0 array and imputation in IMPUTE. Fasting glucose was regressed on SNP dosage data in each ethnic group adjusting for age, gender, MESA study center, and ethnic-specific principal components. SNPs from the three gene regions with the strongest associations to fasting glucose in previous Caucasian GWAS (MTNR1B / GCK / G6PC2) were examined in depth. There was limited power to replicate associations in other ethnic groups due to smaller allele frequencies and limited sample size; SNP associations may also have differed across ethnic groups due to differing linkage disequilibrium patterns with causal variants. rs10830963 in MTNR1B and rs4607517 in GCK demonstrated consistent magnitude and direction of association with fasting glucose across ethnic groups, although the associations were often not nominally significant. In conclusion, certain SNPs in MTNR1B and GCK demonstrate consistent effects across four racial and ethnic groups, narrowing the putative region for these causal variants.     

iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies

Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait-associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework, iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. Data-driven computational experiments convey how informative annotations improve single-nucleotide polymorphism (SNP) selection performance while emphasizing robustness of iFunMed to noninformative annotations. Application to Framingham Heart Study data indicates that iFunMed is able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.     

Using genome‐wide pathway analysis to unravel the etiology of complex diseases

Several genome‐wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene‐gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway‐classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well‐defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single‐locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used. Genet. Epidemiol. 33:419–431, 2009. © 2009 Wiley‐Liss, Inc.     

A replication study examining novel common single nucleotide polymorphisms identified through a prostate cancer genome-wide association study in a Japanese population

Five novel prostate cancer risk loci were identified in a recent genome-wide association study (GWAS) of Japanese persons (Takata et al., Nat Genet. 2010;42(9):751-754). Those authors proposed that apart from population-specific linkage disequilibrium patterns, limitations of GWAS single nucleotide polymorphism (SNP) prioritization and/or study design could explain the lack of identification of these loci in GWAS previously conducted among Caucasians. Thus, the authors undertook a replication study in 1,357 prostate cancer patients and 1,403 healthy Australian males of European descent (2004-2008). The rs12653946 SNP at 5p15 was found to be significantly associated with prostate cancer risk (odds ratio = 1.20, 95% confidence interval: 1.07, 1.34; P = 0.002). On the basis of linkage disequilibrium calculations, the rs12653946 SNP represents an independent locus, distinct from the previously identified TERT-CLPTM1L cancer nexus region. Further, analysis from AceView (Thierry-Mieg and Thierry-Mieg, Genome Biol. 2006;7(suppl 1):S12) indicated that rs12653946 falls within the intron of a testis-expressed gene strongly predicted to translate a conceptual 8.1-kilodalton protein named tojy.aApr07. The authors' findings suggest that follow-up of apparently ethnicity-specific risk associations are warranted in order to highlight risk-associated loci for experimental studies and for incorporation into future risk prediction models for prostate cancer.