Discordant Haplotype Sequencing Identifies Functional Variants at the 2q33 Breast Cancer Risk Locus

Abstract

The findings from genome-wide association studies hold enormous potential for novel insight into disease mechanisms. A major challenge in the field is to map these low risk association signals to their underlying functional sequence variants (FSVs). Simple sequence study designs are insufficient, as the vast numbers of statistically comparable variants and a limited knowledge of non-coding regulatory elements complicate prioritization. Furthermore, large sample sizes are typically required for adequate power to identify the initial association signals. One important question is whether similar sample sizes need to be sequenced to identify the FSVs. Here, we present a proof of principle example of an extreme discordant design to map FSVs within the 2q33 low-risk breast cancer locus. Our approach employed DNA sequencing of a small number of discordant haplotypes to efficiently identify candidate-FSVs. Our results were consistent with those from a 2000-fold larger, traditional imputation-based fine-mapping study. To prioritize further, we used expression-quantitative trait locus (eQTL) analysis of RNA sequencing from breast tissues, gene regulation annotations from the ENCODE consortium, and functional assays for differential enhancer activities. Notably, we implicate three regulatory variants at 2q33 that target CASP8 (rs3769823, rs3769821 in CASP8, and rs10197246 in ALS2CR12) as functionally relevant. We conclude that nested discordant haplotype sequencing is a promising approach to aid mapping of low-risk association loci. The ability to include more efficient sequencing designs into mapping efforts presents an opportunity for the field to capitalize on the potential of association loci and accelerate translation of association signals to their underlying FSVs.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

“…The remaining 34 associated genes are located at known breast cancer susceptibility loci (Tables 2–3). Among them, 23 have not yet been implicated as genes responsible for association signals identified at these loci through expression quantitative trait loci (eQTL) and/or functional studies, and do not harbor GWAS or fine-mapping identified risk variants (Table 2), while the other eleven ( KLHDC7A 7 , ALS2CR12 31 , CASP8 31,32 , ATG10 9 , SNX32 33 , STXBP4 34,35 , ZNF404 8 , ATP6AP1L 9 , RMND1 17, L3MBTL3 6 , and RCCD1 10 ) had been reported as potential causal genes at breast cancer susceptibility loci or harbor GWAS or fine-mapping identified risk variants (Table 3). Except for RP11–73O6.3 and L3MBTL3 , there was no evidence of heterogeneity (I2<0.2) across the iCOGS, OncoArray, and GWAS datasets included in our analyses (Supplementary Table 3).…”

Polymorphisms in the Gene Encoding Caspase 8 May Predict the Response to First-Line Platinum-Based Chemotherapy in Locally Advanced or Advanced Non-Small-Cell Lung Cancer

“…In the literature, there are single reports presenting SNPs of the CASP-8 gene as prognostic factors in lung cancer patients. In fact, the available data (from breast tissue) indicate that it is the AA and not the GG genotype of the CASP-8 gene that is usually associated with lower (and therefore generally unfavorable, apoptosis-inhibiting) expression of the encoded protein [ 42 ]. However, it should be taken into account that this gene may be regulated differently in different tissues and different types of cancer.…”

Association of caspase 8 promoter variants and haplotypes with the risk of breast cancer and its molecular profile in an Iranian population: A case‐control study

“…A study in the United Kingdom and the United States has indicated that rs3769821 can increase the risk of breast cancer up to 17% (OR, 1.17; 95% CI, 1.05‐1.30; P = 0.0032) . As the functional analysis has pointed out the relationship between the risk allele and expression level in breast cancer, rs3769821 may be a potential diagnostic and prognostic factor for breast cancer . To confirm the results, large well‐designed cohort studies are essential.…”