Characterising <i>cis</i>-regulatory variation in the transcriptome of histologically normal and tumour-derived pancreatic tissues

Zhang, Mingfeng; Lykke‐Andersen, Søren; Xiao, Wenming; Hoskins, Jason W.; Zhang, Xijun; Rost, Lauren; Collins, Irene; Bunt, Martijn van de; Jia, Jinping; Parikh, Hemang; Zhang, Tongwu; Song, Lei; Jermusyk, Ashley; Chung, Charles C.; Zhu, Bin; Zhou, Weiyin; Matters, Gail L.; Kurtz, Robert C.; Yeager, Meredith; Jensen, Torben Heick; Brown, Kevin M.; Ongen, Halit; Bamlet, William R.; Murray, Bradley A.; McCarthy, Mark I.; Chanock, Stephen J.; Chatterjee, Nilanjan; Wolpin, Brian M.; Smith, Jill P.; Olson, Sara H.; Petersen, Gloria M.; Shi, Jianxin; Ámundadóttir, Laufey T.

doi:10.1136/gutjnl-2016-313146

Cited by 27 publications

(31 citation statements)

References 54 publications

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“…Of these eGenes, 4 are in common across race: PSPHL , GSTT2 , EFHD1 , and SLC16A3 . Expressions of PSPHL and GSTT2 have been previously reported to be governed by respective cis-deletions and serve as distinguishing biomarkers for race [21–24]. The majority of significant eQTLs in both the AA and WW samples were found in cis-association with respective eGenes.…”

Section: Resultsmentioning

confidence: 99%

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Bhattacharya

García‐Closas

Olshan

et al. 2019

Preprint

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1 Background: The relationship between germline genetic variation and breast cancer 2 survival is largely unknown, especially in understudied minority populations who often 3 have poorer survival. Genome-wide association studies (GWAS) have interrogated 4 breast cancer survival but often are underpowered due to subtype heterogeneity and 5 many clinical covariates and detect loci in non-coding regions that are difficult to interpret. 6 Transcriptome-wide association studies (TWAS) show increased power in detecting 7 functionally-relevant loci by leveraging expression quantitative trait loci (eQTLs) from 8 external reference panels in relevant tissues. However, ancestry-or race-specific 9 reference panels may be needed to draw correct inference in ancestrally-diverse cohorts. 10 Such panels for breast cancer are lacking. 12Results: We provide a framework for TWAS for breast cancer in diverse populations, 13 using data from the Carolina Breast Cancer Study (CBCS), a North Carolina population-14 based cohort that oversampled black women. We perform eQTL analysis for 406 breast 15 cancer-related genes to train race-stratified predictive models of tumor expression from 16 germline genotypes. Using these models, we impute expression in independent data from 17 CBCS and TCGA, accounting for sampling variability in assessing performance. These 18 models are not applicable across race, and their predictive performance varies across 19 tumor subtype. Within CBCS (ܰ = 3,828), at a false discovery-adjusted significance of 20 0.10 and stratifying for race, we identify associations in black women near AURKA, 21 CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. 22Conclusions: We show that carefully implemented and thoroughly validated TWAS is an 24 efficient approach for understanding the genetics underpinning breast cancer outcomes 25 in diverse populations. 26 27 Keywords: transcriptome-wide analysis (TWAS); breast cancer; expression quantitative 28 trait loci (eQTL); survival; polygenic traits 29 4 Background 30 Breast cancer remains the most common cancer among women in the world [1]. Breast 31 cancer tends to be more aggressive in young women and African American women, 32 though underlying germline determinants of poor outcomes are not well-studied. Cohorts 33 that represent understudied minority populations, like the Carolina Breast Cancer Study 34 (CBCS), have identified differences in healthcare access, socioeconomics, and 35 environmental exposures associated with disparities in outcome [2-4], but more targeted 36 genomic studies are necessary to interrogate these disparities from a biologic and genetic 37 perspective. 38 39 Few genome-wide association studies (GWAS) have studied the relationship between 40 germline variation and survival outcomes in breast cancer, with most focusing instead on 41 genetic predictors of risk [5,6]. Recently, GWAS have shown evidence of association 42 between candidate common germline variants and breast cancer survival, but these 43 studies are often underpow...

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Section: Resultsmentioning

confidence: 99%

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Bhattacharya

García‐Closas

Olshan

et al. 2019

Preprint

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show abstract

“…Of these eGenes, 4 are in common across race: PSPHL, GSTT2, EFHD1, and SLC16A3. Expression levels of PSPHL and GSTT2 have been previously reported to be governed by respective cis-deletions and serve as distinguishing biomarkers for race [22][23][24][25]. The majority of significant eQTLs in both the AA and WW samples were found in cis-association with respective eGenes.…”

Section: Race-specific Germline Eqtl Analysismentioning

confidence: 95%

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

et al. 2020

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Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry-or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

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“…Following quality control, we evaluated the significance of allelic imbalance using a binomial test in each individual level, comparing the observed to the subject-and genotype-specific expected allele ratios (Ongen et al 2014), while accounting for base-specific mapping bias (Lappalainen et al 2013;Zhang et al 2018). In addition, the effect size of allelic expression (AE, defined as |0.5-Reference ratio|) was calculated.…”

Section: Allele-specific Expressionmentioning

confidence: 99%

“…To assess the enrichment of cis-eQTL in putative functional elements of primary melanocytes, we collected the DNase-seq and ChIP-seq data from the Epigenome Roadmap Project (http:// www.roadmapepigenomics.org) (Roadmap Epigenomics Consortium et al 2015). For each putative functional element, we merged peak callings from all samples into one, and all the significant melanocyte eQTL SNP-Gene pairs were used for the enrichment analyses using a similar method to a recent publication (Zhang et al 2018). Briefly, we performed randomizations for testing whether an eQTL SNP set is enriched for given histone mark regions.…”

Section: Assessing Enrichment In Putative Functional Elementsmentioning

confidence: 99%

Cell-type–specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes

Zhang

Choi

et al. 2018

Genome Res.

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Most expression quantitative trait locus (eQTL) studies to date have been performed in heterogeneous tissues as opposed to specific cell types. To better understand the cell-type-specific regulatory landscape of human melanocytes, which give rise to melanoma but account for <5% of typical human skin biopsies, we performed an eQTL analysis in primary melanocyte cultures from 106 newborn males. We identified 597,335 cis-eQTL SNPs prior to linkage disequilibrium (LD) pruning and 4997 eGenes (FDR < 0.05). Melanocyte eQTLs differed considerably from those identified in the 44 GTEx tissue types, including skin. Over a third of melanocyte eGenes, including key genes in melanin synthesis pathways, were unique to melanocytes compared to those of GTEx skin tissues or TCGA melanomas. The melanocyte data set also identified trans-eQTLs, including those connecting a pigmentation-associated functional SNP with four genes, likely through cis-regulation of IRF4. Melanocyte eQTLs are enriched in cis-regulatory signatures found in melanocytes as well as in melanoma-associated variants identified through genome-wide association studies. Melanocyte eQTLs also colocalized with melanoma GWAS variants in five known loci. Finally, a transcriptome-wide association study using melanocyte eQTLs uncovered four novel susceptibility loci, where imputed expression levels of five genes (ZFP90, HEBP1, MSC, CBWD1, and RP11-383H13.1) were associated with melanoma at genome-wide significant P-values. Our data highlight the utility of lineage-specific eQTL resources for annotating GWAS findings, and present a robust database for genomic research of melanoma risk and melanocyte biology.

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Characterising cis-regulatory variation in the transcriptome of histologically normal and tumour-derived pancreatic tissues

Cited by 27 publications

References 54 publications

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Cell-type–specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes

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