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Multiple breast cancer risk variants are associated with differential transcript isoform expression in tumors.

Caswell JL, Camarda R, Zhou AY, Huntsman S, Hu D, Brenner SE, Zaitlen N, Goga A, Ziv E - Hum. Mol. Genet. (2015)

Bottom Line: A subset of these SNPs are associated with quantitative expression of nearby genes, but the functional effects of the majority remain unknown.Six SNPs were associated with differential transcript expression of seven nearby genes at FDR < 0.05 (BABAM1, DCLRE1B/PHTF1, PEX14, RAD51L1, SRGAP2D and STXBP4).Lastly, at two loci, we identified the likely causal SNP for the alternative splicing event, and at one, functionally validated the effect of that SNP on alternative splicing using a minigene reporter assay.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center and, Department of Medicine, Division of Medical Oncology, Stanford University, Stanford, CA, USA and caswell@stanford.edu.

No MeSH data available.


Related in: MedlinePlus

Flowchart for determining splicing QTL associations. We identified 13 SNP-gene associations through exon, junction and whole-transcript association tests with risk-associated SNPs; several associations were identified by multiple methods. After excluding SNP-gene associations that could not be corroborated with other tests, that could be related to the presence of pseudogenes or paralogs or that could have derived from mapping bias to the reference genome, seven SNP-gene associations remained.
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DDV432F1: Flowchart for determining splicing QTL associations. We identified 13 SNP-gene associations through exon, junction and whole-transcript association tests with risk-associated SNPs; several associations were identified by multiple methods. After excluding SNP-gene associations that could not be corroborated with other tests, that could be related to the presence of pseudogenes or paralogs or that could have derived from mapping bias to the reference genome, seven SNP-gene associations remained.

Mentions: We used the RNA-sequencing (RNA-seq) data and matched germline genotypes for 358 estrogen receptor (ER)-positive breast tumors and 109 ER-negative breast tumors from TCGA. For each of the breast cancer raSNPs, we searched for differential transcript isoform expression of nearby genes (Supplementary Material, Table S1), adjusting for overall gene expression, global expression variability (16,17) and genetic ancestry. We used three complementary approaches, testing the association between raSNPs and (1) rank-normalized reads per kilobase per million mapped reads (RPKM) mapping to each exon, (2) rank-normalized reads per million mapped reads (RPM) mapping to each exon–exon junction and (3) rank-normalized expression estimates of reconstructed transcripts of each annotated isoform, as generated by the RSEM algorithm using UCSC transcripts (chosen as its output is available through TCGA) (3) (Supplementary Material, Tables S2–S4). We identified 13 associations with 10 raSNPs using these methods at FDR < 0.05, including 9 exon associations, 8 junction associations and 6 whole-transcript associations; several splicing QTLs were identified by more than one approach (Fig. 1). Q–Q plots showed deviation from normality at the extremes of the P-value distributions (Supplementary Material, Fig. S1). When the analysis was repeated in the smaller set of ER-negative tumors, we identified four associations with four raSNPs, including two exon associations, two junction associations and two transcript associations (Supplementary Material, Table S5), all of which were also identified in the ER-positive tumors.Figure 1.


Multiple breast cancer risk variants are associated with differential transcript isoform expression in tumors.

Caswell JL, Camarda R, Zhou AY, Huntsman S, Hu D, Brenner SE, Zaitlen N, Goga A, Ziv E - Hum. Mol. Genet. (2015)

Flowchart for determining splicing QTL associations. We identified 13 SNP-gene associations through exon, junction and whole-transcript association tests with risk-associated SNPs; several associations were identified by multiple methods. After excluding SNP-gene associations that could not be corroborated with other tests, that could be related to the presence of pseudogenes or paralogs or that could have derived from mapping bias to the reference genome, seven SNP-gene associations remained.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664170&req=5

DDV432F1: Flowchart for determining splicing QTL associations. We identified 13 SNP-gene associations through exon, junction and whole-transcript association tests with risk-associated SNPs; several associations were identified by multiple methods. After excluding SNP-gene associations that could not be corroborated with other tests, that could be related to the presence of pseudogenes or paralogs or that could have derived from mapping bias to the reference genome, seven SNP-gene associations remained.
Mentions: We used the RNA-sequencing (RNA-seq) data and matched germline genotypes for 358 estrogen receptor (ER)-positive breast tumors and 109 ER-negative breast tumors from TCGA. For each of the breast cancer raSNPs, we searched for differential transcript isoform expression of nearby genes (Supplementary Material, Table S1), adjusting for overall gene expression, global expression variability (16,17) and genetic ancestry. We used three complementary approaches, testing the association between raSNPs and (1) rank-normalized reads per kilobase per million mapped reads (RPKM) mapping to each exon, (2) rank-normalized reads per million mapped reads (RPM) mapping to each exon–exon junction and (3) rank-normalized expression estimates of reconstructed transcripts of each annotated isoform, as generated by the RSEM algorithm using UCSC transcripts (chosen as its output is available through TCGA) (3) (Supplementary Material, Tables S2–S4). We identified 13 associations with 10 raSNPs using these methods at FDR < 0.05, including 9 exon associations, 8 junction associations and 6 whole-transcript associations; several splicing QTLs were identified by more than one approach (Fig. 1). Q–Q plots showed deviation from normality at the extremes of the P-value distributions (Supplementary Material, Fig. S1). When the analysis was repeated in the smaller set of ER-negative tumors, we identified four associations with four raSNPs, including two exon associations, two junction associations and two transcript associations (Supplementary Material, Table S5), all of which were also identified in the ER-positive tumors.Figure 1.

Bottom Line: A subset of these SNPs are associated with quantitative expression of nearby genes, but the functional effects of the majority remain unknown.Six SNPs were associated with differential transcript expression of seven nearby genes at FDR < 0.05 (BABAM1, DCLRE1B/PHTF1, PEX14, RAD51L1, SRGAP2D and STXBP4).Lastly, at two loci, we identified the likely causal SNP for the alternative splicing event, and at one, functionally validated the effect of that SNP on alternative splicing using a minigene reporter assay.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center and, Department of Medicine, Division of Medical Oncology, Stanford University, Stanford, CA, USA and caswell@stanford.edu.

No MeSH data available.


Related in: MedlinePlus