全转录关联研究确定与膀胱癌症风险相关的基因-动脉网

Transcriptome-wide association study identifies genes associated with bladder cancer risk

Nature 等信源发布 2025-01-09 13:28

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Abstract

摘要

Genome-wide association studies (GWAS) have detected several susceptibility variants for urinary bladder cancer, but how gene regulation affects disease development remains unclear. To extend GWAS findings, we conducted a transcriptome-wide association study (TWAS) using PrediXcan to predict gene expression levels in whole blood using genome-wide genotype data for 6180 bladder cancer cases and 5699 controls included in the database of Genotypes and Phenotypes (dbGaP).

全基因组关联研究（GWAS）已检测到膀胱癌的几种易感变异，但基因调控如何影响疾病发展仍不清楚。为了扩展GWAS的发现，我们使用PrediXcan进行了转录组范围的关联研究（TWAS），以使用基因型和表型数据库（dbGaP）中包含的6180例膀胱癌病例和5699例对照的全基因组基因型数据预测全血中的基因表达水平。

Logistic regression was used to estimate adjusted gene-level odds ratios (OR) per 1-standard deviation higher expression with 95% confidence intervals (CI) for bladder cancer risk. We further assessed associations for individual single-nucleotide polymorphisms (SNPs) used to predict expression levels and proximal loci for genes identified in gene-level analyses with false-discovery rate (FDR) correction.

Logistic回归用于估计膀胱癌风险的95%置信区间（CI）下每1个标准差较高表达的校正基因水平优势比（OR）。我们进一步评估了单个单核苷酸多态性（SNP）的关联，这些单核苷酸多态性用于预测基因水平分析中鉴定的基因的表达水平和近端基因座，并进行了错误发现率（FDR）校正。

TWAS identified four genes for which expression levels were associated with bladder cancer risk: .

TWAS鉴定了四个表达水平与膀胱癌风险相关的基因：。

SLC39A3

(OR = 0.91, CI = 0.87–0.95, FDR = 0.015),

（或：=0.91，CI：=0.87-0.95，FDR：=0.015），

ZNF737

ZNF737型

(OR = 0.91, CI = 0.88–0.95, FDR = 0.016),

（或：0.91，CI:0.88-0.95，FDR:0.016），

FAM53A

家庭53a

(OR = 1.09, CI = 1.05–1.14, FDR = 0.022), and

1.05–1.14，FDR==0.022），以及

PPP1R2

(OR = 1.09, CI = 1.05–1.13, FDR = 0.049). Findings from this TWAS enhance our understanding of how genetically-regulated gene expression affects bladder cancer development and point to potential prevention and treatment targets.

（OR=1.09，CI=1.05-1.13，FDR=0.049）。这项研究的结果增强了我们对基因调控基因表达如何影响膀胱癌发展的理解，并指出了潜在的预防和治疗目标。

Introduction

导言

About 82,000 new cases of urinary bladder cancer are diagnosed annually in the United States

美国每年诊断出约82000例新的膀胱癌病例

. Given its high recurrence rate and costly therapy, bladder cancer carries a considerable morbidity and financial burden

鉴于其高复发率和昂贵的治疗，膀胱癌的发病率和经济负担相当大

. Genome-wide association studies (GWAS) have detected several common genetic variants associated with bladder cancer risk

.全基因组关联研究（GWAS）已检测到与膀胱癌风险相关的几种常见遗传变异

, yet our understanding of the inherited susceptibility remains incomplete, currently explaining far less of the heritability estimated from twin studies

，然而我们对遗传易感性的理解仍然不完整，目前解释双胞胎研究估计的遗传力要少得多

. Many GWAS-identified risk variants are located in non-coding regions, making it difficult to determine their function in disease development

。许多GWAS识别的风险变体位于非编码区，因此很难确定它们在疾病发展中的功能

. Additionally, GWAS can have a high multiple-testing burden, requiring large sample sizes to detect modest effect sizes, and associations may be detected only for genotyped single nucleotide polymorphisms (SNPs) correlated with causal variants

此外，GWAS可能具有很高的多重测试负担，需要较大的样本量才能检测到适度的效应量，并且只能检测与因果变异相关的基因型单核苷酸多态性（SNP）的关联

Transcriptome-wide association studies (TWAS) have become a valuable way to investigate disease associations with gene expression

转录组范围的关联研究（TWAS）已成为研究疾病与基因表达关联的有价值的方法

. TWAS leverages reference data on tissue-specific expression quantitative trait loci (eQTLs) to predict gene expression levels using germline genotype data

。TWAS利用组织特异性表达数量性状基因座（eQTL）的参考数据，使用种系基因型数据预测基因表达水平

. TWAS offers some advantages over GWAS. Focusing on gene-level associations can lower the multiple-testing burden, minimize the influence of correlated variants, and improve statistical power by aggregating eQTL information

。TWAS比GWAS具有一些优势。关注基因水平的关联可以降低多重测试负担，最小化相关变异的影响，并通过聚集eQTL信息来提高统计能力

. The tissue-specificity of TWAS can contribute to distinct insights into biological mechanisms. Koutros et al.

。TWAS的组织特异性可以有助于对生物学机制的独特见解。库特罗斯等人。

conducted a multi-tissue TWAS based on up to 48 tissue types using bladder cancer cases available from The Cancer Genome Atlas (TCGA) and controls from the Genotype-Tissue Expression (GTEx) project. Multi-tissue approaches may be justified because large repositories of gene expression data collected from normal bladder tissue are lacking.

使用可从癌症基因组图谱（TCGA）获得的膀胱癌病例和基因型组织表达（GTEx）项目的对照，基于多达48种组织类型进行了多组织TWAS。多组织方法可能是合理的，因为缺乏从正常膀胱组织收集的大量基因表达数据库。

GTEx, for example, currently includes eQTL data for only a small number (.

例如，GTEx目前仅包含少量的eQTL数据（。

= 21) of normal bladder tissue samples. However, aggregating expression data from dozens of tissue types unrelated to bladder physiology likely identifies highly non-specific eQTLs and may dilute signals apparent only in the target tissue. In contrast, we chose to focus on circulating expression data exclusively.

21）正常膀胱组织样本。然而，汇总来自数十种与膀胱生理学无关的组织类型的表达数据可能会识别出高度非特异性的eQTL，并且可能会稀释仅在靶组织中明显的信号。相反，我们选择专门关注循环表达数据。

The GTEx eQTL database includes a relatively large number (.

GTEx eQTL数据库包含相对较大的数字（。

= 670) of whole blood samples, which is sufficient for robust TWAS inference, and this approach may inform potential targets for blood-based cancer screening tests currently under development

670）的全血样本，这足以进行可靠的TWAS推断，这种方法可能为目前正在开发的基于血液的癌症筛查测试的潜在目标提供信息

. We utilized PrediXcan, a popular TWAS method, to predict gene expression levels in whole blood using genome-wide genotype data from several completed bladder cancer case-control studies

。我们利用流行的TWAS方法PrediXcan，使用来自几项已完成的膀胱癌病例对照研究的全基因组基因型数据预测全血中的基因表达水平

Results

结果

After quality control (QC), 5403 bladder cancer cases and 4672 controls were included. We predicted cis-eQTL-regulated gene expression levels of 7233 genes from 171,054 SNPs after QC filtering (Fig.

质量控制（QC）后，纳入5403例膀胱癌病例和4672例对照。我们预测QC过滤后来自171054个SNP的7233个基因的顺式eQTL调控的基因表达水平（图）。

Fig. 1

图1

The TWAS was performed using genotype data from a two-phase GWAS funded by the National Cancer Institute (NCI) comprised of 6180 bladder cancer cases and 5699 controls (dbGaP study accession number phs000346.v2.p2). After genotype imputation and QC filtering, a total of 5403 cases and 4672 controls were included in logistic regression models that adjusted for gender, age, and the first three principal components of genetic ancestry..

使用来自国家癌症研究所（NCI）资助的两阶段GWAS的基因型数据进行TWAS，该GWAS由6180例膀胱癌病例和5699例对照组成（dbGaP研究登录号phs000346.v2.p2）。经过基因型插补和QC过滤后，共有5403例病例和4672例对照被纳入逻辑回归模型，该模型根据性别，年龄和遗传祖先的前三个主要成分进行了调整。。

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A total of four genes (

共有四个基因(

SLC39A3

on 19p13.3,

在19p13.3上，

ZNF737

ZNF737型

on 19p12,

在19p12，

FAM53A

家庭53a

on 4p16.3, and

在4p16.3上，以及

PPP1R2

on 3q29) were identified to be associated with bladder cancer risk with FDR < 0.05 (Table

在3q29）被确定与FDR<0.05的膀胱癌风险相关（表

; Fig.

；图。

). Higher whole blood expression of

)。全血表达较高

FAM53A

家庭53a

and

和

PPP1R2

was associated with higher odds of bladder cancer, while higher expression of

与膀胱癌的几率较高有关，而

SLC39A3

and

和

ZNF737

ZNF737型

was associated with lower odds of bladder cancer. We did not find evidence of heterogeneity according to gender and age (Supplemental Fig.

与膀胱癌的几率较低有关。根据性别和年龄，我们没有发现异质性的证据（补充图）。

Table 1 Predicted gene expression levels identified by TWAS with an FDR-adjusted P value < 0.05.

表1由TWAS鉴定的预测基因表达水平，FDR调整后的P值<0.05。

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Fig. 2

图2

Manhattan plot of TWAS associations between predicted gene expression and bladder cancer risk. The four labeled genes were statistically significant with FDR correction.

预测基因表达与膀胱癌风险之间TWAS关联的曼哈顿图。通过FDR校正，这四个标记的基因具有统计学意义。

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In single-SNP analyses of 44 cis-eQTLs variants used by PrediXcan to predict gene expression for

在PrediXcan用于预测基因表达的44个顺式eQTL变体的单SNP分析中

SLC39A3

ZNF737

ZNF737型

FAM53A

家庭53a

, and

，以及

PPP1R2

, we identified 14 SNPs associated with bladder cancer at

，我们在

< 1.0E–5 (Table

<1.0e–5（表

; Supplementary Fig.

；补充图。

)

. No SNPs of

.没有SNP

ZNF737

ZNF737型

reached this threshold and no SNPs near any of the four genes reached the genome-wide statistical significance threshold of

达到该阈值，并且四个基因中任何一个附近的SNP均未达到全基因组统计显着性阈值

< 5.0E–8.

<5.0E–8。

Table 2 Fourteen cis-eQTL SNPs of the TWAS-identified genes were associated with bladder cancer risk (

表2 TWAS鉴定基因的十四个顺式eQTL SNP与膀胱癌风险相关(

< 1.0E−5).

<1.0E−5）。

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GWAS-loci mapping analyses revealed a GWAS-identified bladder cancer risk SNP near those used to predict expression of

GWAS基因座作图分析显示，GWAS鉴定的膀胱癌风险SNP接近用于预测

FAM53A

家庭53a

(rs798766 located in

（rs798766位于

TACC3

near

近的

FGFR3

)

. Five of the

.其中五个

FAM53A

家庭53a

SNPs listed in Table

表中列出的SNP

are highly correlated (r

高度相关（r

> 0.9) with rs798766 in the 1000 Genomes CEU population: rs798726, rs798741, rs798744, rs798756, and rs2166580 (Fig.

>在1000个基因组CEU人群中，rs798766为0.9）：rs798726，rs798741，rs798744，rs798756和rs2166580（图）。

). We did not identify any GWAS-identified bladder cancer risk SNPs located within 1 Mb of

)。我们没有发现任何GWAS鉴定的膀胱癌风险SNP位于1 Mb以内

SLC39A3

ZNF737

ZNF737型

, and

，以及

PPP1R2

Fig. 3

图3

Heatmap of pairwise squared correlations (linkage disequilibrium) of SNPs near

SNP附近成对平方相关（连锁不平衡）的热图

FAM53A

家庭53a

(4p16.3) in the 1000 Genomes CEU population. rs798766 is a GWAS-identified bladder cancer risk SNP and the other SNPs are cis-eQTLs for

（4p16.3）在1000个基因组的CEU人群中。rs798766是GWAS鉴定的膀胱癌风险SNP，其他SNP是顺式eQTL

FAM53A

家庭53a

in whole blood found to be associated with bladder cancer risk in our TWAS.

在我们的TWAS中发现全血与膀胱癌风险相关。

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Discussion

讨论

We performed a TWAS of bladder cancer risk among individuals of European descent leveraging publicly available genotype and transcriptome data, identifying four genes (

我们利用公开可用的基因型和转录组数据，在欧洲血统的个体中进行了膀胱癌风险的TWAS，确定了四个基因(

SLC39A3

ZNF737

ZNF737型

FAM53A

家庭53a

, and

，以及

PPP1R2

) with genetically predicted expression levels in whole blood associated with bladder cancer risk. A known GWAS-identified bladder cancer susceptibility SNP, rs798766 of

)基因预测的全血表达水平与膀胱癌风险相关。一个已知的GWAS鉴定的膀胱癌易感性SNP，rs798766

TACC3

(near

（近

FGFR3

), was among SNPs associated with

)，是与

FAM53A

家庭53a

expression in whole blood. Our findings reveal a potential role of blood expression-related genetics in bladder cancer development.

全血中的表达。我们的发现揭示了血液表达相关遗传学在膀胱癌发展中的潜在作用。

We identified a novel association between lower circulating expression of

我们发现了一种新的低循环表达之间的关联

SLC39A3

and bladder cancer risk.

和膀胱癌风险。

SLC39A3

is a member of the solute carrier family 39 of zinc transporters that function in the cellular homeostasis of zinc

是锌转运蛋白溶质载体家族39的成员，该家族在锌的细胞稳态中起作用

, an essential trace element involved in multiple physiological processes, including immune function and cellular proliferation

，一种参与多种生理过程的必需微量元素，包括免疫功能和细胞增殖

. SLC39 family members are Zrt, Irt-like Proteins (ZIP) that function to increase cytosolic zinc concentrations

SLC39家族成员是Zrt，Irt样蛋白（ZIP），其功能是增加胞质锌浓度

. Common SNPs of genes in the SLC39 family have been detected to be associated with bladder cancer risk. Karagas et al. reported a higher risk of bladder cancer for carriers of the C allele of a missense variant of

。已检测到SLC39家族基因的常见SNP与膀胱癌风险相关。卡拉加斯等人报道，错义变体C等位基因携带者患膀胱癌的风险更高

SLC39A2

(rs2234636), specifically among those with high toenail arsenic concentrations

（rs2234636），特别是脚趾甲砷浓度高的人群

. A candidate gene study by Wu et al. found that four SNPs of

Wu等人的一项候选基因研究发现

SLC39A11

were associated with bladder cancer risk

与膀胱癌风险有关

. Although our study did not identify other SLC39 family genes associated with bladder cancer risk, previous studies of tumor expression have suggested that several SLC39 family genes may be involved in bladder cancer prognosis. For example, a pan-cancer TCGA analysis found that tumor overexpression of .

尽管我们的研究没有发现与膀胱癌风险相关的其他SLC39家族基因，但先前的肿瘤表达研究表明，几种SLC39家族基因可能与膀胱癌的预后有关。例如，泛癌TCGA分析发现肿瘤过度表达。

SLC39A3

SLC39A5

, and

，以及

SLC39A11

was associated with better overall survival after bladder cancer diagnosis, whereas tumor overexpression of

与膀胱癌诊断后更好的总体生存率相关，而肿瘤过度表达

SLC39A2

SLC39A8

SLC39A9

, and

，以及

SLC39A14

was associated with poorer survival

与较差的生存率相关

We also found an inverse association between whole blood expression of

我们还发现全血表达与

ZNF737

ZNF737型

and bladder cancer risk.

和膀胱癌风险。

ZNF737

ZNF737型

is a member of the large family of zinc finger proteins (ZNFs), transcription factors frequently studied in cancer

是锌指蛋白（ZNFs）大家族的成员，锌指蛋白是癌症中经常研究的转录因子

. Numerous ZNF family genes have been documented to function as tumor promoters or suppressors across different cancer types

许多ZNF家族基因已被证明在不同的癌症类型中起着肿瘤启动子或抑制剂的作用

ZNF737

ZNF737型

itself is not well characterized. Our finding of higher

本身没有很好的特征。我们发现更高

ZNF737

ZNF737型

expression in association with lower bladder cancer risk is consistent with tumor suppressive activity previously documented for

与较低的膀胱癌风险相关的表达与先前记录的肿瘤抑制活性一致

ZNF750

ZNF750型

in esophageal squamous cell carcinoma

食管鳞状细胞癌

ZNF24

锌F24

in breast cancer

在乳腺癌中

, and

，以及

ZNF185

ZNF185型

in prostate cancer

前列腺癌

Higher whole blood expression of

全血表达较高

FAM53A

家庭53a

, located on 4p16.3, was associated with higher bladder cancer risk. This locus also reached statistical significance in the multi-tissue bladder cancer TWAS conducted by Koutros et al.

位于4p16.3，与较高的膀胱癌风险相关。该基因座在Koutros等人进行的多组织膀胱癌TWAS中也达到了统计学意义。

, but they did not report the direction of association or indicate whether the association was tissue-specific. Among the SNPs we used to predict

，但他们没有报告关联的方向，也没有指出关联是否是组织特异性的。在我们用来预测的SNPs中

FAM53A

家庭53a

expression, four are in perfect linkage disequilibrium with rs798766 (r

表达，其中四个与rs798766（r

= 1 in the 1000 Genomes CEU population), a known GWAS-identified bladder cancer risk SNP

在1000个基因组的CEU人群中有1个），一个已知的GWAS确定的膀胱癌风险SNP

. Consistent with GWAS findings for rs798766, the minor allele of these SNPs was associated with a higher bladder cancer risk in our data. Previous pre-clinical studies have linked higher expression of neighboring genes to

与GWAS对rs798766的发现一致，在我们的数据中，这些SNP的次要等位基因与较高的膀胱癌风险相关。先前的临床前研究已经将邻近基因的高表达与

FAM53A

家庭53a

on 4p16.3, including

在4p16.3上，包括

TACC3

and

和

FGFR3

, to bladder cancer development and progression

，膀胱癌的发展和进展

. In particular,

.特别是，

FGFR3

mutations and fusions have been well-characterized in bladder tumors

突变和融合在膀胱肿瘤中已得到很好的表征

. Additional research is needed to understand whether circulating

。需要进一步研究以了解是否循环

FAM53A

家庭53a

expression could be a marker for 4p16.3 rearrangements and subsequent prognosis.

表达可能是4p16.3重排和随后预后的标志。

Our TWAS also suggests that higher circulating

我们的TWAS还表明，更高的循环

PPP1R2

expression is associated with higher bladder cancer risk.

表达与较高的膀胱癌风险相关。

PPP1R2

encodes one of the regulatory (inhibitory) subunits of protein phosphatase 1 (PP1), which plays a critical role in glycogen metabolism and several basic cellular functions

编码蛋白磷酸酶1（PP1）的调节（抑制）亚基之一，该亚基在糖原代谢和几种基本细胞功能中起关键作用

. PP1 inhibitors are thought to counteract the tumor-suppressive function of retinoblastoma protein (pRb)

PP1抑制剂被认为可以抵消视网膜母细胞瘤蛋白（pRb）的肿瘤抑制功能

. This is generally consistent with our observation of an association between higher expression of an inhibitory subunit and higher cancer risk.

这与我们观察到的抑制性亚基的高表达与较高的癌症风险之间的关联基本一致。

PPP1R2

is expressed in many human cancer cell lines

在许多人类癌细胞系中表达

, indicating involvement in fundamental oncogenic processes. Studies of the role of

，表明参与了基本的致癌过程。研究

PPP1R2

in bladder cancer specifically, however, are lacking.

然而，特别是在膀胱癌中缺乏。

This study has some limitations, including those that are inherent to all TWAS

这项研究有一些局限性，包括所有TWA固有的局限性

. We used genotype data to predict gene expression in whole blood rather than in bladder tissue because the sample size of bladder tissue in GTEx is small (

。我们使用基因型数据来预测全血而不是膀胱组织中的基因表达，因为GTEx中膀胱组织的样本量很小(

= 21), greatly limiting its utility as a TWAS reference panel. TWAS findings based on expression data from normal bladder tissue and bladder tumor tissue, when available, may be interpreted more directly in the context of what is known about organ physiology or tumor environment

=21），极大地限制了其作为TWAS参考面板的实用性。基于正常膀胱组织和膀胱肿瘤组织表达数据的TWAS发现（如果可用）可以在已知的器官生理学或肿瘤环境的背景下更直接地解释

. On the other hand, TWAS findings based on the circulating transcriptome may be useful in suggesting new targets for emerging blood-based early cancer detection tools.

另一方面，基于循环转录组的TWAS发现可能有助于为新兴的基于血液的早期癌症检测工具提出新的目标。

The dbGaP release did not include information on tumor subtypes or histologies, preventing us from distinguishing muscle-invasive from non-muscle-invasive bladder cancer and urothelial from non-urothelial cancers. We expect some of the associations we identified may be specific to the incidence of early-stage, low-grade disease and thus potentially more useful targets for early detection.

dbGaP版本不包括有关肿瘤亚型或组织学的信息，使我们无法区分肌肉浸润性膀胱癌和非肌肉浸润性膀胱癌以及尿路上皮癌和非尿路上皮癌。我们预计我们确定的一些关联可能特定于早期低度疾病的发病率，因此可能是早期发现的更有用的目标。

Several previous GWAS of bladder cancer risk have adjusted for smoking status.

之前的几项膀胱癌风险GWAS已根据吸烟状况进行了调整。

, one of the strongest disease risk factors

，是最强的疾病风险因素之一

, but this covariate was unavailable to us. It seems unlikely that additionally adjusting for smoking would have a major impact on our results given that the genes we identified are not among those found to be associated with smoking behaviors or nicotine metabolism

，但我们无法获得这个协变量。鉴于我们发现的基因不属于与吸烟行为或尼古丁代谢相关的基因，因此似乎不太可能对吸烟进行额外的调整对我们的结果产生重大影响

After genotype imputation and QC, the bladder cancer GWAS dataset included 96.6% of the cis-eQTL SNPs in the version of PredictDB for the PrediXcan elastic net models. We did not search for correlated SNPs to use as proxies and thus may be missing signals for some genes. However, it appears that most excluded SNPs were rare or otherwise failed our QC filtering.

在基因型插补和QC之后，膀胱癌GWAS数据集包括PredictDB版本中96.6%的顺式eQTL SNP，用于PrediXcan弹性网络模型。我们没有搜索相关的SNP作为代理，因此可能缺少某些基因的信号。但是，似乎大多数被排除的SNP很少见，或者我们的QC过滤失败。

Single-SNP analyses that we performed secondary to gene-level analyses for genes that met an FDR threshold are reported without multiple comparison correction. Although no single-SNP association reached genome-wide statistical significance, we evaluated only 44 SNPs in total for these analyses, which may not warrant such a stringent threshold to prevent false positives.

据报道，我们对满足FDR阈值的基因进行了继发于基因水平分析的单SNP分析，而没有进行多重比较校正。尽管没有单个SNP关联达到全基因组统计显着性，但我们总共仅评估了44个SNP用于这些分析，这可能无法保证如此严格的阈值来防止假阳性。

We included only individuals of European ancestry in our TWAS, potentially limiting generalizability of our findings to other populations. Future studies of larger size should include genotype and transcriptome data from other ancestral populations.

我们在TWAS中仅包括欧洲血统的个体，这可能会限制我们的发现对其他人群的普遍性。未来较大规模的研究应包括来自其他祖先种群的基因型和转录组数据。

Materials and methods

材料和方法

Genotype data and study populations

基因型数据和研究人群

Genome-wide genotype data for 6180 bladder cancer (in situ and invasive) cases and 5699 controls were ascertained from a two-phase multi-center GWAS of bladder cancer risk accessed from the database of Genotypes and Phenotypes (dbGaP); study accession number phs000346.v2.p2)

从基因型和表型数据库（dbGaP）访问的膀胱癌风险的两阶段多中心GWAS中确定了6180例膀胱癌（原位和侵袭性）病例和5699例对照的全基因组基因型数据；研究登录号phs000346.v2.p2）

. Phase I samples (NCI-GWAS1) included participants from the Spanish Bladder Cancer Study (SBCS), New England Bladder Cancer Study - Maine and Vermont (NEBCS-ME/VT), Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), and American Cancer Society Cancer Prevention Study II Nutrition Cohort (CPS-II).

第一阶段样本（NCI-GWAS1）包括来自西班牙膀胱癌研究（SBCS），新英格兰膀胱癌研究-缅因州和佛蒙特州（NEBCS-ME/VT），α-生育酚，β-胡萝卜素癌症预防研究（ATBC），前列腺癌，肺癌，结直肠癌和卵巢癌筛查试验（PLCO）和美国癌症协会癌症预防研究II营养队列（CPS-II）的参与者。

Phase II samples (NCI-GWAS2) included participants from the New England Bladder Cancer Study—New Hampshire (NEBCS-NH), Los Angeles Bladder Cancer Study (LABCS), French Center for Research on Prostate Diseases (CeRePP), French Bladder Study (FBCS), Brescia Bladder Cancer Study (BBCS), European Prospective Investigation Into Cancer and Nutrition Study (EPIC), Health Professionals Follow-up Study (HPFS), Women’s Health Initiative (WHI), and the Nurses’ Health Study (NHS).

第二阶段样本（NCI-GWAS2）包括来自新罕布什尔州新英格兰膀胱癌研究（NEBCS-NH），洛杉矶膀胱癌研究（LABCS），法国前列腺疾病研究中心（CeRePP），法国膀胱研究（FBCS），布雷西亚膀胱癌研究（BBCS），欧洲癌症和营养前瞻性研究（EPIC），卫生专业人员随访研究（HPFS），妇女健康倡议（WHI）和护士健康研究（NHS）的参与者。

. A summary of the number of SNPs, genotyping platforms used, and the number of bladder cancer cases and controls from included studies is provided in Supplementary Table

补充表中提供了SNP数量，使用的基因分型平台以及纳入研究的膀胱癌病例和对照数量的摘要

Quality control and genotype imputation

质量控制和基因型插补

We conducted pre- and post-imputation genotype quality control (QC) using PLINK

我们使用PLINK进行了插补前后基因型质量控制（QC）

following guidelines by Chia et al.

遵循Chia等人的指导方针。

. Pre-imputation QC was implemented separately for each genotyping platform. At the individual level, we excluded individuals with more than 5% missing genotypes, with genetic heterozygosity exceeding an F-coefficient threshold (F > 0.15 or F < -0.15), and whose first or second principal components of genetic ancestry were beyond six standard errors from the mean for Europeans using ancestral references from the HapMap 3 Genome Reference Panel.

对于每个基因分型平台，分别实施预插补QC。在个体水平上，我们排除了缺失基因型超过5%，遗传杂合性超过F系数阈值（F > 0.15 或F<-0.15），并且使用HapMap 3基因组参考小组的祖先参考，其遗传血统的第一或第二主成分与欧洲人的平均值相比超过了六个标准误差。

. At the SNP level, we removed variants with minor allele frequency (MAF) less than 5%, with more than 5% missing genotypes across individuals, and with evidence of deviation from Hardy-Weinberg equilibrium (

在SNP水平上，我们删除了次要等位基因频率（MAF）小于5%，个体间缺失基因型超过5%的变异，并且有证据表明偏离了Hardy-Weinberg平衡(

< 1.0E−3). We also performed a case/control nonrandom missingness test to exclude SNPs with evidence of missingness differences between cases and controls (

<1.0E-3）。我们还进行了病例/对照非随机缺失测试，以排除具有病例和对照之间缺失差异证据的SNP(

< 1.0E−4). Genotype imputation was conducted using the Michigan Imputation Server

<1.0E-4）。基因型插补是使用密歇根插补服务器进行的

, employing the Minimac4 algorithm and the 1000 Genome Project Phase 3 version 5 reference panel

，使用Minimac4算法和1000基因组计划第3阶段第5版参考面板

. Phasing was performed using Eagle v2.4. We retained potentially related individuals (pairwise expected shared genotypes ≥ 0.2) for imputation but excluded one individual from each pair for association analyses. Post-imputation QC retained only SNPs with an imputation accuracy R

.使用Eagle v2.4进行定相。我们保留了潜在相关的个体（成对预期的共享基因型≥0.2）进行插补，但从每对个体中排除了一个人进行关联分析。插补后QC仅保留插补精度为R的SNP

≥ 0.3.

Transcriptome-wide association study

转录组范围的关联研究

We used PrediXcan

我们使用了PrediXcan

to predict gene expression levels in whole blood for bladder cancer cases and controls from the imputed SNPs that passed QC filtering. PrediXcan trains a prediction model using reference datasets comprising both genome-wide germline genotype and tissue-specific transcriptome data, permitting estimation of eQTL-regulated gene expression levels in an external genotyped dataset without requiring transcriptome data from the same samples.

从通过QC过滤的估算SNP预测膀胱癌病例和对照的全血中的基因表达水平。PrediXcan使用包含全基因组种系基因型和组织特异性转录组数据的参考数据集训练预测模型，从而可以估计外部基因分型数据集中eQTL调节的基因表达水平，而不需要来自相同样本的转录组数据。

Elastic net model weights were ascertained from PredictDB, a publicly available database that contains pre-computed PrediXcan weights for 48 tissue types from the Genotype-Tissue Expression (GTEx) project.

弹性净模型权重是从PredictDB确定的，PredictDB是一个公开可用的数据库，其中包含来自基因型组织表达（GTEx）项目的48种组织类型的预先计算的PrediXcan权重。

. We used the GTEx version 8 reference transcriptome data for whole blood and aligned both the GTEx and dbGaP bladder cancer case-control genotype data to the GRCh38 genome assembly

。我们使用GTEx版本8的全血参考转录组数据，并将GTEx和dbGaP膀胱癌病例对照基因型数据与GRCh38基因组装配进行了比对

. All predicted gene expression levels were standardized by subtracting the mean and dividing by the standard deviation before regression analysis to reduce heterogeneity in expression scales.

在回归分析之前，通过减去平均值并除以标准差来标准化所有预测的基因表达水平，以减少表达量表的异质性。

Logistic regression was used to estimate odds ratios (OR) with 95% confidence intervals (CI) for the association of gene expression with bladder cancer case-control status adjusting for gender, age, and the first three principal components of genetic ancestry estimated from a panel of 2,318 ancestry-informative SNPs.

Logistic回归用于估计基因表达与膀胱癌病例对照状态之间关联的95%置信区间（CI）的优势比（OR），根据性别，年龄和遗传祖先的前三个主要成分进行调整。2318个祖先信息SNP。

. To account for multiple comparisons, a false-discovery rate (FDR) threshold of 0.05 was employed to define statistical significance. Stratified analyses according to gender and age groups (≤ 50, 50–60, 60–70, > 70 years) were conducted given the substantial disparity in disease risk for these groups.

为了解释多重比较，采用0.05的错误发现率（FDR）阈值来定义统计显着性。鉴于这些人群的疾病风险存在很大差异，根据性别和年龄组（≤50、50-60、60-70、>>70岁）进行了分层分析。

. Association analyses were performed using R

关联分析使用R

; all P values are two-sided.

；所有P值都是双面的。

We further investigated genes found to be associated with bladder cancer risk at the FDR threshold by performing single-SNP analyses focused on the set of variants used to predict gene expression levels with a logistic regression model similar to that used in gene-level analyses. A threshold of

我们进一步研究了在FDR阈值下与膀胱癌风险相关的基因，方法是使用与基因水平分析相似的逻辑回归模型，对用于预测基因表达水平的一组变体进行单SNP分析。阈值为

< 5.0E−8 was used to define genome-wide statistical significance. SNPs with

<5.0E-8用于定义全基因组统计显着性。SNP与

< 1.0E−5 were also reported. We mapped SNPs within a 1 Mb region upstream or downstream of the TWAS-identified genes with a listing of GWAS-identified bladder cancer risk variants reported in at least two previous GWAS studies for bladder cancer risk included in the NHGRI-EBI GWAS Catalog

还报道了<1.0E-5。我们绘制了TWAS鉴定基因上游或下游1 Mb区域内的SNP，并列出了至少两项先前的GWAS研究中报道的NHGRI-EBI GWAS目录中包含的膀胱癌风险的GWAS鉴定膀胱癌风险变异

. Squared correlations (linkage disequilibrium) between GWAS-identified SNPs and our TWAS-identified SNPs were calculated using the Ensembl Linkage Disequilibrium Calculator

。使用Ensembl连锁不平衡计算器计算了GWAS识别的SNP和我们的TWAS识别的SNP之间的平方相关性（连锁不平衡）

for the Northern and Western Europeans from Utah (CEU) reference population.

犹他州（CEU）的北欧和西欧参考人群。

Data availability

数据可用性

All human data used in this study are de-identified and publicly available upon request and approval through dbGaP (accession number phs000346.v2.p2).

本研究中使用的所有人类数据均已取消识别，并可通过dbGaP（登录号phs000346.v2.p2）根据要求和批准公开获得。

References

参考文献

Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023.

Siegel，R.L.，Miller，K.D.，Wagle，N.S。和Jemal，A。癌症统计，2023年。

CA Cancer J. Clin.

CA Cancer J.Clin。

, 17–48 (2023).

PubMed

Google Scholar

谷歌学者

Botteman, M. F., Pashos, C. L., Redaelli, A., Laskin, B. & Hauser, R. The health economics of bladder cancer.

Botteman，M.F.，Pashos，C.L.，Redaelli，A.，Laskin，B。＆Hauser，R。膀胱癌的健康经济学。

Pharmacoeconomics

药物经济学

, 1315–1330 (2003).

PubMed

Google Scholar

谷歌学者

Mossanen, M. et al. Evaluating the cost of surveillance for non-muscle-invasive bladder cancer: an analysis based on risk categories.

Mossanen，M.等人，《评估非肌肉浸润性膀胱癌的监测成本：基于风险类别的分析》。

World J. Urol.

, 2059–2065 (2019).

PubMed

MATH

数学

Google Scholar

谷歌学者

Rothman, N. et al. A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

Rothman，N。等人。一项膀胱癌的多阶段全基因组关联研究确定了多个易感基因座。

Nat. Genet.

纳特，基因。

, 978–984 (2010).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Kiemeney, L. A. et al. A sequence variant at 4p16. 3 confers susceptibility to urinary bladder cancer.

Kiemeney，L.A。等人。4p16的序列变体。3赋予膀胱癌易感性。

Nat. Genet.

纳特，基因。

, 415–419 (2010).

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Figueroa, J. D. et al. Genome-wide association study identifies multiple loci associated with bladder cancer risk.

全基因组关联研究确定了与膀胱癌风险相关的多个基因座。

Hum. Mol. Genet.

分子遗传学。

, 1387–1398 (2014).

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

MacArthur, J. et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS catalog).

MacArthur，J.等人，《已发表的全基因组关联研究的新NHGRI-EBI目录》（GWAS目录）。

Nucleic Acids Res.

核酸研究。

, D896–901 (2017).

，D896-901（2017）。

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland.

Lichtenstein，P.等人。瑞典、丹麦和芬兰双胞胎队列癌症病因分析中的环境和遗传因素。

N Engl. J. Med.

N英语。医学杂志。

343

, 78–85 (2000).

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Mucci, L. A. et al. Familial risk and heritability of cancer among twins in nordic countries.

Mucci，L.A.等人，《北欧国家双胞胎患癌症的家族风险和遗传性》。

Jama

Crap

315

, 68–76 (2016).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Li, B. & Ritchie, M. D. From GWAS to gene: transcriptome-wide association studies and other methods to functionally understand GWAS discoveries.

Li，B。＆Ritchie，M.D。从GWAS到基因：转录组范围的关联研究和其他功能性理解GWAS发现的方法。

Front. Genet.

前面。基因。

, 713230 (2021).

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.

Hindorff，L.A。等人。全基因组关联基因座对人类疾病和性状的潜在病因和功能意义。

Proc. Natl. Acad. Sci.

Proc。纳特尔。阿卡德。滑雪。

106

, 9362–9367 (2009).

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome.

ENCODE项目联盟。人类基因组中DNA元素的综合百科全书。

Nature

自然

489

, 57 (2012).

ADS

Google Scholar

谷歌学者

Tam, V. et al. Benefits and limitations of genome-wide association studies.

Tam，V。等人。全基因组关联研究的益处和局限性。

Nat. Rev. Genet.

《遗传学》杂志。

, 467–484 (2019).

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies.

Wainberg，M.等人。转录组关联研究的机遇和挑战。

Nat. Genet.

纳特，基因。

, 592–599 (2019).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Westra, H-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations.

Westra，H-J.等人。反式eQTL作为已知疾病关联的推定驱动因素的系统鉴定。

Nat. Genet.

纳特，基因。

, 1238–1243 (2013).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Chatzinakos, C., Georgiadis, F. & Daskalakis, N. P. GWAS meets transcriptomics: from genetic letters to transcriptomic words of neuropsychiatric risk.

Chatzinakos，C.，Georgiadis，F。＆Daskalakis，N.P。GWAS符合转录组学：从遗传字母到神经精神风险的转录组学单词。

Neuropsychopharmacology

神经精神药理学

, 255 (2021).

PubMed

Google Scholar

谷歌学者

Li, D., Liu, Q. & Schnable, P. S. TWAS results are complementary to and less affected by linkage disequilibrium than GWAS.

Li，D.，Liu，Q。＆Schnable，P。S。TWAS结果与GWAS相比，对连锁不平衡的补充和影响较小。

Plant. Physiol.

工厂。生理学。

186

, 1800–1811 (2021).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Pain, O. et al. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics.

通过将表达数据与全基因组关联统计相结合，获得了对自闭症谱系障碍病因的新见解。

Biol. Psychiatry

生物精神病学

, 265–273 (2019).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Koutros, S. et al. Genome-wide Association study of bladder Cancer reveals New Biological and Translational insights.

膀胱癌的全基因组关联研究揭示了新的生物学和翻译见解。

Eur. Urol.

小时。

, 127–137 (2023).

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Crosby, D. et al. Early detection of cancer.

Crosby，D.等人，《癌症的早期发现》。

Science

科学

375

, eaay9040 (2022).

，eaay9040（2022）。

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Garcia-Closas, M. et al. A genome-wide association study of bladder cancer identifies a new susceptibility locus within SLC14A1, a urea transporter gene on chromosome 18q12. 3.

Garcia Closas，M。等人。一项全基因组膀胱癌关联研究确定了SLC14A1（染色体18q12上的尿素转运蛋白基因）内的一个新的易感基因座。3

Hum. Mol. Genet.

分子遗传学。

, 4282–4289 (2011).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Mailman, M. D. et al. The NCBI dbGaP database of genotypes and phenotypes.

Mailman，M.D。等人。NCBI dbGaP基因型和表型数据库。

Nat. Genet.

纳特，基因。

, 1181–1186 (2007).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Tryka, K. A. et al. NCBI’s database of genotypes and phenotypes: dbGaP.

Tryka，K.A。等人。NCBI的基因型和表型数据库：dbGaP。

Nucleic Acids Res.

核酸研究。

, D975–D979 (2014).

，D975–D979（2014）。

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results.

Pruim，R。J。等人。LocusZoom：全基因组关联扫描结果的区域可视化。

Bioinformatics

生物信息学

, 2336–2337 (2010).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Jeong, J. & Eide, D. J. The SLC39 family of zinc transporters.

Jeong，J。和Eide，D。J。SLC39锌转运蛋白家族。

Mol. Aspects Med.

Mol.Aspects Med。

, 612–619 (2013).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Bafaro, E., Liu, Y., Xu, Y. & Dempski, R. E. The emerging role of zinc transporters in cellular homeostasis and cancer.

Bafaro，E.，Liu，Y.，Xu，Y。＆Dempski，R.E。锌转运蛋白在细胞稳态和癌症中的新兴作用。

Signal. Transduct. Target. Ther.

信号。Transduct公司。目标。他们。

, 1–12 (2017).

Google Scholar

谷歌学者

Kambe, T., Taylor, K. M. & Fu, D. Zinc transporters and their functional integration in mammalian cells.

Kambe，T.，Taylor，K.M。＆Fu，D。锌转运蛋白及其在哺乳动物细胞中的功能整合。

J. Biol. Chem.

J.生物学。化学。

296

, 4586 (2021).

Karagas, M. R. et al. SLC39A2 and FSIP1 polymorphisms as potential modifiers of arsenic-related bladder cancer.

Karagas，M.R.等人，SLC39A2和FSIP1多态性是砷相关膀胱癌的潜在修饰因子。

Hum. Genet.

嗯，吉恩。

131

, 453–461 (2012).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Wu, L., Chaffee, K. G., Parker, A. S., Sicotte, H. & Petersen, G. M. Zinc transporter genes and urological cancers: integrated analysis suggests a role for ZIP11 in bladder cancer.

Wu，L.，Chaffee，K.G.，Parker，A.S.，Sicotte，H。＆Petersen，G.M。锌转运蛋白基因与泌尿系统癌症：综合分析表明ZIP11在膀胱癌中的作用。

Tumor Biol.

生物肿瘤。

, 7431–7437 (2015).

CAS

中科院

Google Scholar

谷歌学者

Qu, Y-Y., Guo, R-Y., Luo, M-L. & Zhou, Q. Pan-cancer analysis of the solute carrier family 39 genes in relation to oncogenic, immune infiltrating, and therapeutic targets.

Qu，Y-Y.，Guo，R-Y.，Luo，M-L。＆Zhou，Q。溶质载体家族39个基因与致癌，免疫浸润和治疗靶标的泛癌分析。

Front. Genet.

前面。基因。

, 757582 (2021).

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Jen, J. & Wang, Y-C. Zinc finger proteins in cancer progression.

Jen，J。＆Wang，Y-C。锌指蛋白在癌症进展中的作用。

J. Biomed. Sci.

J.生物识别。科学。

, 1–9 (2016).

MathSciNet

MATH

数学

Google Scholar

谷歌学者

Cassandri, M. et al. Zinc-finger proteins in health and disease.

Cassandri，M.等人，《锌指蛋白与健康和疾病》。

Cell. Death Discov

细胞。死亡迪斯科

, 1–12 (2017).

Google Scholar

谷歌学者

Lin, D-C. et al. Genomic and molecular characterization of esophageal squamous cell carcinoma.

Lin，D-C。等。食管鳞状细胞癌的基因组和分子表征。

Nat. Genet.

纳特，基因。

, 467–473 (2014).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Jia, D. et al. Transcriptional repression of VEGF by ZNF24: mechanistic studies and vascular consequences in vivo.

Jia，D。等人。ZNF24对VEGF的转录抑制：体内机制研究和血管后果。

Blood J. Am. Soc. Hematol.

血液J.Am.Soc.Hematol。

121

, 707–715 (2013).

CAS

中科院

MATH

数学

Google Scholar

谷歌学者

Vanaja, D. K., Cheville, J. C., Iturria, S. J. & Young, C. Y. Transcriptional silencing of zinc finger protein 185 identified by expression profiling is associated with prostate cancer progression.

Vanaja，D.K.，Cheville，J.C.，Iturria，S.J。和Young，C.Y。通过表达谱鉴定的锌指蛋白185的转录沉默与前列腺癌进展相关。

Cancer Res.

癌症研究。

, 3877–3882 (2003).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Meng, X-Y. et al. Association between the TACC3 rs798766 polymorphism and risk of urinary bladder cancer: a synthesis based on current evidence.

Meng，X-Y.等人。TACC3 rs798766多态性与膀胱癌风险之间的关联：基于当前证据的综合。

Dis. Markers

Dis。标记

;

2017

, 859 (2017).

Lin, Z-R., Wang, M-Y., He, S-Y., Cai, Z-M. & Huang, W-R. TACC3 transcriptionally upregulates E2F1 to promote cell growth and confer sensitivity to cisplatin in bladder cancer.

Lin，Z-R.，Wang，M-Y.，He，S-Y.，Cai，Z-M。＆Huang，W-R。TACC3转录上调E2F1以促进细胞生长并赋予膀胱癌对顺铂的敏感性。

Cell. Death Dis.

细胞。死亡Dis。

, 72 (2018).

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Tomlinson, D., Baldo, O., Harnden, P. & Knowles, M. FGFR3 protein expression and its relationship to mutation status and prognostic variables in bladder cancer.

Tomlinson，D.，Baldo，O.，Harnden，P。＆Knowles，M。FGFR3蛋白表达及其与膀胱癌突变状态和预后变量的关系。

J. Pathol.

J.病理学。

213

, 91–98 (2007).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer.

Robertson，A.G.等人，《肌肉浸润性膀胱癌的综合分子表征》。

Cell

细胞

171

, 540–556 (2017).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Williams, S. V., Hurst, C. D. & Knowles, M. A. Oncogenic FGFR3 gene fusions in bladder cancer.

Williams，S.V.，Hurst，C.D。和Knowles，M.A。膀胱癌中的致癌FGFR3基因融合。

Hum. Mol. Genet.

分子遗传学。

, 795–803 (2013).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Connor, J. H. et al. Cellular mechanisms regulating protein phosphatase-1: a key functional interaction between inhibitor-2 and the type 1 protein phosphatase catalytic subunit.

Connor，J.H.等人。调节蛋白磷酸酶-1的细胞机制：抑制剂-2和1型蛋白磷酸酶催化亚基之间的关键功能相互作用。

J. Biol. Chem.

J.生物学。化学。

275

, 18670–18675 (2000).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Heroes, E. et al. The PP1 binding code: a molecular-lego strategy that governs specificity.

Heroes，E.等人，《PP1结合密码：一种控制特异性的分子乐高策略》。

FEBS J.

FEBS J。

280

, 584–595 (2013).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Wang, H. & Brautigan, D. L. A novel transmembrane Ser/Thr kinase complexes with protein phosphatase-1 and inhibitor-2.

Wang，H。＆Brautigan，D.L。一种新型跨膜Ser/Thr激酶复合物与蛋白磷酸酶-1和抑制剂-2。

J. Biol. Chem.

J.生物学。化学。

277

, 49605–49612 (2002).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Felgueiras, J., Jeronimo, C. & Fardilha, M. Protein phosphatase 1 in tumorigenesis: is it worth a closer look?

Felgueiras，J.，Jeronimo，C。＆Fardilha，M。肿瘤发生中的蛋白磷酸酶1：值得仔细研究吗？

Biochim. Biophys. Acta BBA-Rev Cancer

生物化学。生物物理。BBA Rev癌症学报

1874

, 188433 (2020).

CAS

中科院

Google Scholar

谷歌学者

Kolupaeva, V. & Janssens, V. PP1 and PP2A phosphatases–cooperating partners in modulating retinoblastoma protein activation.

Kolupaeva，V。＆Janssens，V。PP1和PP2A磷酸酶-调节视网膜母细胞瘤蛋白活化的合作伙伴。

FEBS J.

FEBS J。

280

, 627–643 (2013).

CAS

中科院

PubMed

Google Scholar

谷歌学者

Takakura, S. et al. Genetic alterations and expression of the protein phosphatase 1 genes in human cancers.

Takakura，S.等人。人类癌症中蛋白磷酸酶1基因的遗传改变和表达。

Int. J. Oncol.

国际J.叔叔。

, 817–824 (2001).

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Rink, M. et al. Smoking and bladder cancer: a systematic review of risk and outcomes.

Rink，M.等人，《吸烟与膀胱癌：风险和结果的系统评价》。

Eur. Urol. Focus

欧洲泌尿科。福库斯

, 17–27 (2015).

PubMed

MATH

数学

Google Scholar

谷歌学者

Saunders, G. R. et al. Genetic diversity fuels gene discovery for tobacco and alcohol use.

遗传多样性促进了烟草和酒精使用的基因发现。

Nature

自然

612

, 720–724 (2022).

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Karlsson Linnér, R. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Karlsson-Linnér，r。等人。对100多万个体的风险耐受性和风险行为进行全基因组关联分析，确定了数百个基因座并具有共同的遗传影响。

Nat. Genet.

纳特，基因。

, 245–257 (2019).

PubMed

Google Scholar

谷歌学者

Fatumo, S. et al. A roadmap to increase diversity in genomic studies.

Fatumo，S.等人，《增加基因组研究多样性的路线图》。

Nat. Med.

自然医学。

, 243–250 (2022).

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses.

Purcell，S。等人。PLINK：用于全基因组关联和基于人群的连锁分析的工具集。

Am. J. Hum. Genet.

Am.J.Hum.Genet。

, 559–575 (2007).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets.

Chang，C.C.等人，《第二代PLINK：迎接更大、更丰富数据集的挑战》。

Gigascience

, s13742–s13015 (2015).

，s13742–s13015（2015）。

Google Scholar

谷歌学者

Chia, R. et al. Identification of genetic risk loci and prioritization of genes and pathways for myasthenia gravis: a genome-wide association study.

Chia，R。等人。重症肌无力遗传风险位点的鉴定以及基因和途径的优先排序：全基因组关联研究。

Proc. Natl. Acad. Sci.

Proc。纳特尔。阿卡德。滑雪。

119

, e2108672119 (2022).

，e2108672119（2022年）。

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations.

国际HapMap 3联盟。整合不同人群中常见和罕见的遗传变异。

Nature

自然

467

, 52 (2010).

ADS

Google Scholar

谷歌学者

Das, S. et al. Next-generation genotype imputation service and methods.

Das，S.等人，《下一代基因型插补服务和方法》。

Nat. Genet.

纳特，基因。

, 1284–1287 (2016).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

1000 Genomes Project Consortium. A global reference for human genetic variation.

1000个基因组计划联盟。人类遗传变异的全球参考。

Nature

自然

526

, 68 (2015).

Google Scholar

谷歌学者

Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes.

Sudmant，P.H.等人，《2504个人类基因组结构变异的综合图谱》。

Nature

自然

526

, 75–81 (2015).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data.

Gamazon，E.R.等人。一种基于基因的关联方法，用于使用参考转录组数据绘制性状。

Nat. Genet.

纳特，基因。

, 1091–1098 (2015).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Lonsdale, J. et al. The genotype-tissue expression (GTEx) project.

Lonsdale，J。等人，《基因型组织表达（GTEx）项目》。

Nat. Genet.

纳特，基因。

, 580–585 (2013).

CAS

中科院

MATH

数学

Google Scholar

谷歌学者

GTEx Consortium, Ardlie, K. G. et al. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans.

GTEx Consortium，Ardlie，K.G.等人。基因型组织表达（GTEx）初步分析：人类多组织基因调控。

Science

科学

348

, 648–660 (2015).

Google Scholar

谷歌学者

Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools.

Kuhn，R.M.，Haussler，D。和Kent，W.J。UCSC基因组浏览器和相关工具。

Brief. Bioinform

简介。生物信息

, 144–161 (2013).

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Shumate, A. & Salzberg, S. L. Liftoff: accurate mapping of gene annotations.

Shumate，A。＆Salzberg，S.L。Liftoff：基因注释的精确定位。

Bioinformatics

生物信息学

, 1639–1643 (2021).

CAS

中科院

PubMed

PubMed Central

公共医学中心

MATH

数学

Google Scholar

谷歌学者

Li, Y. et al. FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data.

Li，Y。等人。FastPop：一种使用遗传数据推断洲际血统的快速主成分衍生方法。

BMC Bioinform.

BMC生物信息。

, 1–8 (2016).

Google Scholar

谷歌学者

Shariat, S. F. et al. The effect of age and gender on bladder cancer: a critical review of the literature.

Shariat，S.F.等人，《年龄和性别对膀胱癌的影响：文献综述》。

BJU Int.

北京和睦家医院。

105

, 300–308 (2010).

PubMed

MATH

数学

Google Scholar

谷歌学者

Dobruch, J. et al. Gender and bladder cancer: a collaborative review of etiology, biology, and outcomes.

Dobruch，J.等人，《性别与膀胱癌：病因学，生物学和结果的合作评论》。

Eur. Urol.

小时。

, 300–310 (2016).

PubMed

MATH

数学

Google Scholar

谷歌学者

R Core Team.

R核心团队。

R: A Language and Environment for Statistical Computing

R：统计计算语言与环境

(R Foundation for Statistical Computing, 2021).

（R统计计算基金会，2021年）。

Welter, D. et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations.

Welter，D。等人，《NHGRI GWAS目录》，SNP性状关联的精选资源。

Nucleic Acids Res.

核酸研究。

, D1001–D1006 (2014).

，D1001–D1006（2014）。

CAS

中科院

PubMed

MATH

数学

Google Scholar

谷歌学者

Cunningham, F. et al. Ensembl 2019.

Cunningham，F。等人，Ensembl 2019。

Nucleic Acids Res.

核酸研究。

, D745–D751 (2019).

，D745–D751（2019）。

CAS

中科院

PubMed

Google Scholar

谷歌学者

Download references

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Acknowledgements

致谢

The authors would like to thank the participants of the bladder cancer GWAS funded by the National Cancer Institute (NCI) at the National Institutes of Health (NIH), available in dbGaP (accession number phs000346.v2.p2). This study was supported by the National Institute of General Medical Sciences at the National Institutes of Health (P20GM104416 and P30GM149408 to M.

作者要感谢由美国国立卫生研究院（NIH）国家癌症研究所（NCI）资助的膀胱癌GWAS的参与者，该研究可在dbGaP（登录号phs000346.v2.p2）中获得。这项研究得到了美国国立卫生研究院国立普通医学研究所的支持（P20GM104416和P30GM149408）。

R. Karagas)..

卡拉加斯先生。

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Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

美国新罕布什尔州汉诺威达特茅斯盖塞尔医学院生物医学数据科学系

Siting Li & Jiang Gui

李四亭、蒋桂

Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

美国新罕布什尔州汉诺威达特茅斯盖塞尔医学院流行病学系

Siting Li, Margaret R. Karagas & Michael N. Passarelli

李，\玛格丽特·R·卡拉加斯\迈克尔·N·帕萨雷利

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S.L. performed research and drafted the manuscript. S.L., J.G., M.R.K. and M.N.P. contributed to conception, research design and manuscript editing. All authors approved the final manuscript.

S、 L.进行了研究并起草了手稿。S、 L.，J.G.，M.R.K.和M.N.P.为概念，研究设计和手稿编辑做出了贡献。所有作者都批准了最终稿件。

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Li, S., Gui, J., Karagas, M.R.

李，S.，桂，J.，卡拉加斯，M.R。

et al.