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Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.

Thibodeau SN, French AJ, McDonnell SK, Cheville J, Middha S, Tillmans L, Riska S, Baheti S, Larson MC, Fogarty Z, Zhang Y, Larson N, Nair A, O'Brien D, Wang L, Schaid DJ - Nat Commun (2015)

Bottom Line: We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals.Of all SNP-gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates.Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905, USA.

ABSTRACT
Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP-gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

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Related in: MedlinePlus

Positional distribution of peak cis-eQTLs and their effect size.Each point represents the peak SNP for each significant target gene (N=88). The β-coefficient obtained by regressing normalized expression levels for each target gene on the number of minor alleles of each SNP genotype adjusted for histologic characteristics and 14 expression principal components is plotted on the y axis and the SNP distance from the TSS or TES on the x axis. For display purposes, the distance between the TES and TSS was normalized to be 100 kb.
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f4: Positional distribution of peak cis-eQTLs and their effect size.Each point represents the peak SNP for each significant target gene (N=88). The β-coefficient obtained by regressing normalized expression levels for each target gene on the number of minor alleles of each SNP genotype adjusted for histologic characteristics and 14 expression principal components is plotted on the y axis and the SNP distance from the TSS or TES on the x axis. For display purposes, the distance between the TES and TSS was normalized to be 100 kb.

Mentions: We then examined the positional distributions and the magnitude of gene dysregulation for each of the peak eQTLs relative to the TSS and TES. In general, we observed a high density of the peak eQTLs in proximity to the TSS and TES of the target genes, with 53 of the 88 (60%) significant peak eQTL signals within 20 kb of at least one of these positions (Fig. 4). However, the distance from the TSS to the peak eQTL signal varied considerably (Fig. 4), ranging from 57 bp to a higher value of ∼1 Mb (not shown). Also shown in Fig. 4 is the magnitude of the eQTL effects (that is, the absolute level of expression differences associated with the SNP genotypes). Generally, those eQTLs associated with larger differences in gene expression clustered near the TSS and TES, while those eQTLs associated with smaller differences were observed further away.


Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.

Thibodeau SN, French AJ, McDonnell SK, Cheville J, Middha S, Tillmans L, Riska S, Baheti S, Larson MC, Fogarty Z, Zhang Y, Larson N, Nair A, O'Brien D, Wang L, Schaid DJ - Nat Commun (2015)

Positional distribution of peak cis-eQTLs and their effect size.Each point represents the peak SNP for each significant target gene (N=88). The β-coefficient obtained by regressing normalized expression levels for each target gene on the number of minor alleles of each SNP genotype adjusted for histologic characteristics and 14 expression principal components is plotted on the y axis and the SNP distance from the TSS or TES on the x axis. For display purposes, the distance between the TES and TSS was normalized to be 100 kb.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663677&req=5

f4: Positional distribution of peak cis-eQTLs and their effect size.Each point represents the peak SNP for each significant target gene (N=88). The β-coefficient obtained by regressing normalized expression levels for each target gene on the number of minor alleles of each SNP genotype adjusted for histologic characteristics and 14 expression principal components is plotted on the y axis and the SNP distance from the TSS or TES on the x axis. For display purposes, the distance between the TES and TSS was normalized to be 100 kb.
Mentions: We then examined the positional distributions and the magnitude of gene dysregulation for each of the peak eQTLs relative to the TSS and TES. In general, we observed a high density of the peak eQTLs in proximity to the TSS and TES of the target genes, with 53 of the 88 (60%) significant peak eQTL signals within 20 kb of at least one of these positions (Fig. 4). However, the distance from the TSS to the peak eQTL signal varied considerably (Fig. 4), ranging from 57 bp to a higher value of ∼1 Mb (not shown). Also shown in Fig. 4 is the magnitude of the eQTL effects (that is, the absolute level of expression differences associated with the SNP genotypes). Generally, those eQTLs associated with larger differences in gene expression clustered near the TSS and TES, while those eQTLs associated with smaller differences were observed further away.

Bottom Line: We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals.Of all SNP-gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates.Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905, USA.

ABSTRACT
Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP-gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

Show MeSH
Related in: MedlinePlus