Comparing the Efficacy of SNP Filtering Methods for Identifying a Single Causal SNP in a Known Association Region

Spencer, Amy; Cox, Angela; Walters, Kevin

doi:10.1111/ahg.12043

Cited by 13 publications

(16 citation statements)

References 31 publications

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“…We assumed that SNPs with a likelihood of <1:100 (ref. 9) in comparison with the most significant SNP for each signal could be excluded from consideration as potentially causative variants. On the basis of the assumption that, within a given signal, the same variant(s) would be driving all observed phenotype associations, we derived the list of most likely causal SNPs for each signal.…”

Section: Resultsmentioning

confidence: 99%

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Dunning¹,

Michailidou²,

et al. 2016

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We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER−) and human ERBB2 (HER2+ or HER2−) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER− tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

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Section: Resultsmentioning

confidence: 99%

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Dunning¹,

Michailidou²,

et al. 2016

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“…SNPs with a relative likelihood ratio of <1:100 compared with the most significant SNP for each iCHAV were excluded from consideration as being potentially causative. 16,30 Eleven SNPs had a relative likelihood ratio of >1:100 compared with the most significant SNP (rs10995201) and hence could not be excluded as causative for the lead signal-these SNPs were all strongly correlated with rs10995201 and span an interval of 31.2 kb (iCHAV1, Figure 1, Table S4). A 12 th SNP, a single base insertion chr10: 64,291,099, was excluded at this threshold, but not strongly so (likelihood ratio~1:600); all other SNPs could be clearly excluded (likelihood ratios < 1:10 12 ).…”

Section: Resultsmentioning

confidence: 99%

Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression

Darabi

McCue

Beesley

et al. 2015

The American Journal of Human Genetics

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Genome-wide association studies have identified SNPs near ZNF365 at 10q21.2 that are associated with both breast cancer risk and mammographic density. To identify the most likely causal SNPs, we fine mapped the association signal by genotyping 428 SNPs across the region in 89,050 European and 12,893 Asian case and control subjects from the Breast Cancer Association Consortium. We identified four independent sets of correlated, highly trait-associated variants (iCHAVs), three of which were located within ZNF365. The most strongly risk-associated SNP, rs10995201 in iCHAV1, showed clear evidence of association with both estrogen receptor (ER)-positive (OR = 0.85 [0.82-0.88]) and ER-negative (OR = 0.87 [0.82-0.91]) disease, and was also the SNP most strongly associated with percent mammographic density. iCHAV2 (lead SNP, chr10: 64,258,684:D) and iCHAV3 (lead SNP, rs7922449) were also associated with ER-positive (OR = 0.93 [0.91-0.95] and OR = 1.06 [1.03-1.09]) and ER-negative (OR = 0.95 [0.91-0.98] and OR = 1.08 [1.04-1.13]) disease. There was weaker evidence for iCHAV4, located 5' of ADO, associated only with ER-positive breast cancer (OR = 0.93 [0.90-0.96]). We found 12, 17, 18, and 2 candidate causal SNPs for breast cancer in iCHAVs 1-4, respectively. Chromosome conformation capture analysis showed that iCHAV2 interacts with the ZNF365 and NRBF2 (more than 600 kb away) promoters in normal and cancerous breast epithelial cells. Luciferase assays did not identify SNPs that affect transactivation of ZNF365, but identified a protective haplotype in iCHAV2, associated with silencing of the NRBF2 promoter, implicating this gene in the etiology of breast cancer.

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“…Table contains information about the top 20 ranked SNPs based on the PPBF values calculated from the full iCOGS data. It should be noted that none of these will necessarily be the causal SNP, although previous simulations show us that, with such a large sample size, there is a high probability that the causal SNP will be highly ranked (Spencer et al., ). We observe that nine of the top 20 ranked SNPs were among the 501 SNPs genotyped (rather than imputed) in the iCOGS data.…”

Section: Resultsmentioning

confidence: 99%

Novel Bayes Factors That Capture Expert Uncertainty in Prior Density Specification in Genetic Association Studies

Spencer

Cox

Lin

et al. 2015

Genetic Epidemiology

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Bayes factors (BFs) are becoming increasingly important tools in genetic association studies, partly because they provide a natural framework for including prior information. The Wakefield BF (WBF) approximation is easy to calculate and assumes a normal prior on the log odds ratio (logOR) with a mean of zero. However, the prior variance (W) must be specified. Because of the potentially high sensitivity of the WBF to the choice of W, we propose several new BF approximations with logOR ∼N(0,W), but allow W to take a probability distribution rather than a fixed value. We provide several prior distributions for W which lead to BFs that can be calculated easily in freely available software packages. These priors allow a wide range of densities for W and provide considerable flexibility. We examine some properties of the priors and BFs and show how to determine the most appropriate prior based on elicited quantiles of the prior odds ratio (OR). We show by simulation that our novel BFs have superior true‐positive rates at low false‐positive rates compared to those from both P‐value and WBF analyses across a range of sample sizes and ORs. We give an example of utilizing our BFs to fine‐map the CASP8 region using genotype data on approximately 46,000 breast cancer case and 43,000 healthy control samples from the Collaborative Oncological Gene‐environment Study (COGS) Consortium, and compare the single‐nucleotide polymorphism ranks to those obtained using WBFs and P‐values from univariate logistic regression.

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Comparing the Efficacy of SNP Filtering Methods for Identifying a Single Causal SNP in a Known Association Region

Cited by 13 publications

References 31 publications

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression

Novel Bayes Factors That Capture Expert Uncertainty in Prior Density Specification in Genetic Association Studies

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